1
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Yang T, Zhang Y, Guo L, Li D, Liu A, Bilal M, Xie C, Yang R, Gu Z, Jiang D, Wang P. Antifreeze Polysaccharides from Wheat Bran: The Structural Characterization and Antifreeze Mechanism. Biomacromolecules 2024; 25:3877-3892. [PMID: 38388358 DOI: 10.1021/acs.biomac.3c00958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Exploring a novel natural cryoprotectant and understanding its antifreeze mechanism allows the rational design of future sustainable antifreeze analogues. In this study, various antifreeze polysaccharides were isolated from wheat bran, and the antifreeze activity was comparatively studied in relation to the molecular structure. The antifreeze mechanism was further revealed based on the interactions of polysaccharides and water molecules through dynamic simulation analysis. The antifreeze polysaccharides showed distinct ice recrystallization inhibition activity, and structural analysis suggested that the polysaccharides were arabinoxylan, featuring a xylan backbone with a majority of Araf and minor fractions of Manp, Galp, and Glcp involved in the side chain. The antifreeze arabinoxylan, characterized by lower molecular weight, less branching, and more flexible conformation, could weaken the hydrogen bonding of the surrounding water molecules more evidently, thus retarding the transformation of water molecules into the ordered ice structure.
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Affiliation(s)
- Tao Yang
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Physiology, Ecology, and Management, Ministry of Agriculture/National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Yining Zhang
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Li Guo
- School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Dandan Li
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
| | - Anqi Liu
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Muhammad Bilal
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Chong Xie
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
| | - Runqiang Yang
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
| | - Zhenxin Gu
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
| | - Dong Jiang
- National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Physiology, Ecology, and Management, Ministry of Agriculture/National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
| | - Pei Wang
- College of Food Science and Technology, Whole Grain Food Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Physiology, Ecology, and Management, Ministry of Agriculture/National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
- The Sanya Institute of Nanjing Agricultural University, Sanya 572024, People's Republic of China
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2
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Calegari Andrade MF, Aluru NR, Pham TA. Nonlinear Effects of Hydrophobic Confinement on the Electronic Structure and Dielectric Response of Water. J Phys Chem Lett 2024; 15:6872-6879. [PMID: 38934582 DOI: 10.1021/acs.jpclett.4c01242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Fundamental studies of the dielectrics of confined water are critical to understand the ion transport across biological and synthetic nanochannels. The relevance of these fundamental studies, however, surmounts the difficulty of probing water's dielectric constant as a function of a fine variation in confinement. In this work, we explore the computational efficiency of machine learning potentials to derive the confinement effects on the dielectric constant, polarization, and dipole moment of water. Our simulations predict an enhancement of the axial dielectric constant of water under extreme confinement, arising from either the formation of ferroelectric structures of ordered water or larger dipole fluctuations facilitated by the disruption of water's H-bond network. Our study highlights the impact of hydrophobic nanoconfinement on the dielectric constant and on the ionic and electronic structure of water molecules, pointing to the importance of geometric flexibility and electronic polarizability to properly model confinement effects on water.
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Affiliation(s)
- Marcos F Calegari Andrade
- Quantum Simulations Group, Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Laboratory for Energy Applications for the Future, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - N R Aluru
- Walker Department of Mechanical Engineering, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Tuan Anh Pham
- Quantum Simulations Group, Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Laboratory for Energy Applications for the Future, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
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3
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Ribaldone C, Casassa S. Born-Oppenheimer Molecular Dynamics with a Linear Combination of Atomic Orbitals and Hybrid Functionals for Condensed Matter Simulations Made Possible. Theory and Performance for the Microcanonical and Canonical Ensembles. J Chem Theory Comput 2024; 20:3954-3975. [PMID: 38648566 PMCID: PMC11104558 DOI: 10.1021/acs.jctc.3c01231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 04/25/2024]
Abstract
The implementation of an original Born-Oppenheimer molecular dynamics module is presented, which is able to perform simulations of large and complex condensed phase systems for sufficiently long time scales at the level of density functional theory with hybrid functionals, in the microcanonical (NVE) and canonical (NVT) ensembles. The algorithm is fully integrated in the Crystal code, a program for quantum mechanical simulations of materials, whose peculiarity stems from the use of atom-centered basis functions within a linear combination of atomic orbitals to describe the wave function. The corresponding efficiency in the evaluation of the exact Fock exchange series has led to the implementation of a rich variety of hybrid density functionals at a low computational cost. In addition, the molecular dynamics implementation benefits also from the effective MPI parallelization of the code, suited to exploit high-performance computing resources available on current generation supercomputer architectures. Furthermore, the information contained in the trajectory of the dynamics is extracted through a series of postprocessing algorithms that provide the radial distribution function, the diffusion coefficient and the vibrational density of states. In this work, we present a detailed description of the theoretical framework and the algorithmic implementation, followed by a critical evaluation of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach, when ice and liquid water simulations are performed in the microcanonical and canonical ensembles.
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Affiliation(s)
- Chiara Ribaldone
- Dipartimento di Chimica, Università
di Torino, via Giuria 5, 10125 Torino, Italy
| | - Silvia Casassa
- Dipartimento di Chimica, Università
di Torino, via Giuria 5, 10125 Torino, Italy
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4
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Unke OT, Stöhr M, Ganscha S, Unterthiner T, Maennel H, Kashubin S, Ahlin D, Gastegger M, Medrano Sandonas L, Berryman JT, Tkatchenko A, Müller KR. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. SCIENCE ADVANCES 2024; 10:eadn4397. [PMID: 38579003 DOI: 10.1126/sciadv.adn4397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
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Affiliation(s)
- Oliver T Unke
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Martin Stöhr
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Stefan Ganscha
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Thomas Unterthiner
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Hartmut Maennel
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Sergii Kashubin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Daniel Ahlin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
- BASLEARN - TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany
| | - Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Joshua T Berryman
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Klaus-Robert Müller
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
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5
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Goloviznina K, Serva A, Salanne M. Formation of Polymer-like Nanochains with Short Lithium-Lithium Distances in a Water-in-Salt Electrolyte. J Am Chem Soc 2024; 146:8142-8148. [PMID: 38486506 DOI: 10.1021/jacs.3c12488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Water-in-salts (WiSs) have recently emerged as promising electrolytes for energy storage applications ranging from aqueous batteries to supercapacitors. Here, ab initio molecular dynamics is used to study the structure of a 21 m LiTFSI WiS. The simulation reveals a new feature, in which the lithium ions form polymer-like nanochains that involve up to 10 ions. Despite the strong Coulombic interaction between them, the ions in the chains are found at a distance of 2.5 Å. They show a drastically different solvation shell compared to that of the isolated ions, in which they share on average two water molecules. The nanochains have a highly transient character due to the low free energy barrier for forming/breaking them. Providing new insights into the nanostructure of WiS electrolytes, our work calls for reevaluating our current knowledge of highly concentrated electrolytes and the impact of the modification of the solvation of active species on their electrochemical performances.
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Affiliation(s)
- Kateryna Goloviznina
- Sorbonne Université, CNRS, Physicochimie des Électrolytes et Nanosystèmes Interfaciaux, F-75005 Paris, France
- Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens, Cedex, France
| | - Alessandra Serva
- Sorbonne Université, CNRS, Physicochimie des Électrolytes et Nanosystèmes Interfaciaux, F-75005 Paris, France
- Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens, Cedex, France
| | - Mathieu Salanne
- Sorbonne Université, CNRS, Physicochimie des Électrolytes et Nanosystèmes Interfaciaux, F-75005 Paris, France
- Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens, Cedex, France
- Institut Universitaire de France (IUF), 75231 Paris, France
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6
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Batista PR, Ducati LC, Autschbach J. Dynamic and relativistic effects on Pt-Pt indirect spin-spin coupling in aqueous solution studied by ab initio molecular dynamics and two- vs four-component density functional NMR calculations. J Chem Phys 2024; 160:114307. [PMID: 38497474 DOI: 10.1063/5.0196853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024] Open
Abstract
Treating 195Pt nuclear magnetic resonance parameters in solution remains a considerable challenge from a quantum chemistry point of view, requiring a high level of theory that simultaneously takes into account the relativistic effects, the dynamic treatment of the solvent-solute system, and the dynamic electron correlation. A combination of Car-Parrinello molecular dynamics (CPMD) and relativistic calculations based on two-component zeroth order regular approximation spin-orbit Kohn-Sham (2c-ZKS) and four-component Dirac-Kohn-Sham (4c-DKS) Hamiltonians is performed to address the solvent effect (water) on the conformational changes and JPtPt1 coupling. A series of bridged PtIII dinuclear complexes [L1-Pt2(NH3)4(Am)2-L2]n+ (Am = α-pyrrolidonate and pivalamidate; L = H2O, Cl-, and Br-) are studied. The computed Pt-Pt coupling is strongly dependent on the conformational dynamics of the complexes, which, in turn, is correlated with the trans influence among axial ligands and with the angle N-C-O from the bridging ligands. The J-coupling is decomposed in terms of dynamic contributions. The decomposition reveals that the vibrational and explicit solvation contributions reduce JPtPt1 of diaquo complexes (L1 = L2 = H2O) in comparison to the static gas-phase magnitude, whereas the implicit solvation and bulk contributions correspond to an increase in JPtPt1 in dihalo (L1 = L2 = X-) and aquahalo (L1 = H2O; L2 = X-) complexes. Relativistic treatment combined with CPMD shows that the 2c-ZKS Hamiltonian performs as well as 4c-DKS for the JPtPt1 coupling.
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Affiliation(s)
- Patrick R Batista
- Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, 05508-000 São Paulo, SP, Brazil
| | - Lucas C Ducati
- Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, 05508-000 São Paulo, SP, Brazil
| | - Jochen Autschbach
- Department of Chemistry, University at Buffalo, State University of New York, Buffalo, New York 14260-3000, USA
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7
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Villard J, Bircher MP, Rothlisberger U. Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder. Chem Sci 2024; 15:4434-4451. [PMID: 38516095 PMCID: PMC10952088 DOI: 10.1039/d3sc05828j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, with a special focus on the performance of all the later generation Minnesota functionals. Findings are contextualized within the current knowledge on DFT for describing bulk water under ambient conditions and compared to experimental data. We find that, contrary to the prevalent idea that local and semilocal functionals overstructure water and underestimate dynamical properties, M06-L, revM06-L, and M11-L understructure water, while MN12-L and MN15-L overdistance water molecules due to weak cohesive effects. This can be attributed to a weakening of the hydrogen bond network, which leads to dynamical fingerprints that are over fast. While most of the hybrid Minnesota functionals (M06, M08-HX, M08-SO, M11, MN12-SX, and MN15) also yield understructured water, their dynamical properties generally improve over their semilocal counterparts. It emerges that exact exchange is a crucial component for accurately describing hydrogen bonds, which ultimately leads to corrections in both the dynamical and structural properties. However, an excessive amount of exact exchange strengthens hydrogen bonds and causes overstructuring and slow dynamics (M06-HF). As a compromise, M06-2X is the best performing Minnesota functional for water, and its D3 corrected variant shows very good structural agreement. From previous studies considering nuclear quantum effects (NQEs), the hybrid revPBE0-D3, and the rung-5 RPA (RPA@PBE) have been identified as the only two approximations that closely agree with experiments. Our results suggest that the M06-2X(-D3) functionals have the potential to further improve the reproduction of experimental properties when incorporating NQEs through path integral approaches. This work provides further proof that accurate modeling of water interactions requires the inclusion of both exact exchange and balanced (non-local) correlation, highlighting the need for higher rungs on Jacob's ladder to achieve predictive simulations of complex biological systems in aqueous environments.
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Affiliation(s)
- Justin Villard
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne CH-1015 Switzerland
| | - Martin P Bircher
- Computational and Soft Matter Physics, Universität Wien Wien A-1090 Austria
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne CH-1015 Switzerland
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8
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Gámez F, Avilés-Moreno JR, Martens J, Berden G, Oomens J, Martínez-Haya B. Vibrational signatures of dynamic excess proton storage between primary amine and carboxylic acid groups. J Chem Phys 2024; 160:094311. [PMID: 38450729 DOI: 10.1063/5.0192331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 02/20/2024] [Indexed: 03/08/2024] Open
Abstract
Ammonium and carboxylic moieties play a central role in proton-mediated processes of molecular recognition, charge transfer or chemical change in (bio)materials. Whereas both chemical groups constitute acid-base pairs in organic salt-bridge structures, they may as well host excess protons in acidic environments. The binding of excess protons often precedes proton transfer reactions and it is therefore of fundamental interest, though challenging from a quantum chemical perspective. As a benchmark for this process, we investigate proton storage in the amphoteric compound 5-aminovaleric acid (AV), within an intramolecular proton bond shared by its primary amine and carboxylic acid terminal groups. Infrared ion spectroscopy is combined with ab initio Molecular Dynamics (AIMD) calculations to expose and rationalize the spectral signatures of protonated AV and its deuterated isotopologues. The dynamic character of the proton bond confers a fluxional structure to the molecular framework, leading to wide-ranging bands in the vibrational spectrum. These features are reproduced with remarkable accuracy by AIMD computations, which serves to lay out microscopic insights into the excess proton binding scenario.
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Affiliation(s)
- F Gámez
- Department of Physical Chemistry, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - J R Avilés-Moreno
- Department of Applied Physical Chemistry, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - J Martens
- FELIX Laboratory, Institute for Molecules and Materials, Radboud University, Toernooiveld 7, 6525ED Nijmegen, The Netherlands
| | - G Berden
- FELIX Laboratory, Institute for Molecules and Materials, Radboud University, Toernooiveld 7, 6525ED Nijmegen, The Netherlands
| | - J Oomens
- FELIX Laboratory, Institute for Molecules and Materials, Radboud University, Toernooiveld 7, 6525ED Nijmegen, The Netherlands
| | - B Martínez-Haya
- Center for Nanoscience and Sustainable Technologies (CNATS), Universidad Pablo de Olavide, 41013 Seville, Spain
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9
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Jiao S, Li J, Qin X, Wan L, Hu W, Yang J. Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves. J Phys Chem A 2024. [PMID: 38430107 DOI: 10.1021/acs.jpca.3c07172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.
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Affiliation(s)
- Shizhe Jiao
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jielan Li
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xinming Qin
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Lingyun Wan
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Wei Hu
- Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jinlong Yang
- Key Laboratory of Precision and Intelligent Chemistry, and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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10
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Bai X, Chen C, Zhao X, Zhang Y, Zheng Y, Jiao Y. Accelerating the Reaction Kinetics of CO 2 Reduction to Multi-Carbon Products by Synergistic Effect between Cation and Aprotic Solvent on Copper Electrodes. Angew Chem Int Ed Engl 2024; 63:e202317512. [PMID: 38168478 DOI: 10.1002/anie.202317512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/30/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Improving the selectivity of electrochemical CO2 reduction to multi-carbon products (C2+ ) is an important and highly challenging topic. In this work, we propose and validate an effective strategy to improve C2+ selectivity on Cu electrodes, by introducing a synergistic effect between cation (Na+ ) and aprotic solvent (DMSO) to the electrolyte. Based on constant potential ab initio molecular dynamics simulations, we first revealed that Na+ facilitates C-C coupling while inhibits CH3 OH/CH4 products via reducing the water network connectivity near the electrode. Furthermore, the water network connectivity was further decreased by introducing an aprotic solvent DMSO, leading to suppression of both C1 production and hydrogen evolution reaction with minimal effect on *OCCO* hydrogenation. The synergistic effect enhancing C2 selectivity was also experimentally verified through electrochemical measurements. The results showed that the Faradaic efficiency of C2 increases from 9.3 % to 57 % at 50 mA/cm2 under a mixed electrolyte of NaHCO3 and DMSO compared to a pure NaHCO3 , which can significantly enhance the selectivity of the C2 product. Therefore, our discovery provides an effective electrolyte-based strategy for tuning CO2 RR selectivity through modulating the microenvironment at the electrode-electrolyte interface.
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Affiliation(s)
- Xiaowan Bai
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Chaojie Chen
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Xunhua Zhao
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 211189, China
| | - Yehui Zhang
- Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 211189, China
| | - Yao Zheng
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
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11
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Schuitemaker A, Koziara KB, Raiteri P, Gale JD, Demichelis R. New model for aspartic acid species in aqueous calcium carbonate growth environments: challenges and perspectives. Phys Chem Chem Phys 2024; 26:4909-4921. [PMID: 38261361 DOI: 10.1039/d3cp04674e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The lack of experimental data on the dynamics of aspartic acid species in water for its range of protonation states and the details of their atomic-level interaction with aqueous calcium carbonate species is a driver for accurate force field development. A classical model that is consistent with the few pieces of experimental data available and with first principles calculations has been developed. The complex dynamics of the aspartate anions relevant to biomineralization and calcium carbonate crystal growth has been explored in water, providing a quantitative description of solvation structure and free energies, including conformational free energy profiles and pairing free energies. The model has been used to probe the structure and dynamics of aqueous calcium aspartate homo- and hetero-chiral clusters, confirming their unlikelihood due to weak and water-mediated interactions. This supports the hypothesis that the formation of such clusters, observed while growing vaterite in the presence of acidic chiral amino acids, is favoured by the presence of the crystal surface.
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Affiliation(s)
- Alicia Schuitemaker
- Australian Research Council Centre of Excellence in Exciton Science, School of Chemistry, University of Sydney, Sydney, New South Wales, Australia
- The University of Sydney Nano Institute, University of Sydney, Sydney, New South Wales, Australia
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, 6845 Perth, Western Australia, Australia.
| | - Katarzyna B Koziara
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, 6845 Perth, Western Australia, Australia.
| | - Paolo Raiteri
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, 6845 Perth, Western Australia, Australia.
| | - Julian D Gale
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, 6845 Perth, Western Australia, Australia.
| | - Raffaella Demichelis
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, 6845 Perth, Western Australia, Australia.
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12
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Ricard TC, Zhu X, Iyengar SS. Capturing Weak Interactions in Surface Adsorbate Systems at Coupled Cluster Accuracy: A Graph-Theoretic Molecular Fragmentation Approach Improved through Machine Learning. J Chem Theory Comput 2023. [PMID: 38019639 DOI: 10.1021/acs.jctc.3c00955] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
The accurate and efficient study of the interactions of organic matter with the surface of water is critical to a wide range of applications. For example, environmental studies have found that acidic polyfluorinated alkyl substances, especially perfluorooctanoic acid (PFOA), have spread throughout the environment and bioaccumulate into human populations residing near contaminated watersheds, leading to many systemic maladies. Thus, the study of the interactions of PFOA with water surfaces became important for the mitigation of their activity as pollutants and threats to public health. However, theoretical study of the interactions of such organic adsorbates on the surface of water, and their bulk concerted properties, often necessitates the use of ab initio methods to properly incorporate the long-range electronic properties that govern these extended systems. Notable theoretical treatments of "on-water" reactions thus far have employed hybrid DFT and semilocal DFT, but the interactions involved are weak interactions that may be best described using post-Hartree-Fock theory. Here, we aim to demonstrate the utility of a graph-theoretic approach to molecular fragmentation that accurately captures the critical "weak" interactions while maintaining an efficient ab initio treatment of the long-range periodic interactions that underpin the physics of extended systems. We apply this graph-theoretical treatment to study PFOA on the surface of water as a model system for the study of weak interactions seen in the wide range of surface interactions and reactions. The approach divides a system into a set of vertices, that are then connected through edges, faces, and higher order graph theoretic objects known as simplexes, to represent a collection of locally interacting subsystems. These subsystems are then used to construct ab initio molecular dynamics simulations and for computing multidimensional potential energy surfaces. To further improve the computational efficiency of our graph theoretic fragmentation method, we use a recently developed transfer learning protocol to construct the full system potential energy from a family of neural networks each designed to accurately model the behavior of individual simplexes. We use a unique multidimensional clustering algorithm, based on the k-means clustering methodology, to define our training space for each separate simplex. These models are used to extrapolate the energies for molecular dynamics trajectories at PFOA water interfaces, at less than one-tenth the cost as compared to a regular molecular fragmentation-based dynamics calculation with excellent agreement with couple cluster level of full system potential energies.
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Affiliation(s)
- Timothy C Ricard
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Xiao Zhu
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Srinivasan S Iyengar
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
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13
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Kar R, Mandal S, Thakkur V, Meyer B, Nair NN. Speeding-up Hybrid Functional-Based Ab Initio Molecular Dynamics Using Multiple Time-stepping and Resonance-Free Thermostat. J Chem Theory Comput 2023; 19:8351-8364. [PMID: 37933121 DOI: 10.1021/acs.jctc.3c00964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) has become a workhorse for studying the structure, dynamics, and reactions in condensed matter systems. Currently, AIMD simulations are primarily carried out at the level of generalized gradient approximation (GGA), which is at the second rung of DFT functionals in terms of accuracy. Hybrid DFT functionals, which form the fourth rung in the accuracy ladder, are not commonly used in AIMD simulations as the computational cost involved is 100 times or higher. To facilitate AIMD simulations with hybrid functionals, we propose here an approach using multiple time stepping with adaptively compressed exchange operator and resonance-free thermostat, that could speed up the calculations by ∼30 times or more for systems with a few hundred of atoms. We demonstrate that by achieving this significant speed up and making the compute time of hybrid functional-based AIMD simulations at par with that of GGA functionals, we are able to study several complex condensed matter systems and model chemical reactions in solution with hybrid functionals that were earlier unthinkable to be performed.
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Affiliation(s)
- Ritama Kar
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
| | - Sagarmoy Mandal
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nägelsbachstr. 25, Erlangen 91052, Germany
- Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 1, Erlangen 91058, Germany
| | - Vaishali Thakkur
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
| | - Bernd Meyer
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nägelsbachstr. 25, Erlangen 91052, Germany
- Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 1, Erlangen 91058, Germany
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur 208016, India
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14
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Palos E, Caruso A, Paesani F. Consistent density functional theory-based description of ion hydration through density-corrected many-body representations. J Chem Phys 2023; 159:181101. [PMID: 37947509 DOI: 10.1063/5.0174577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
Delocalization error constrains the accuracy of density functional theory in describing molecular interactions in ion-water systems. Using Na+ and Cl- in water as model systems, we calculate the effects of delocalization error in the SCAN functional for describing ion-water and water-water interactions in hydrated ions, and demonstrate that density-corrected SCAN (DC-SCAN) predicts n-body and interaction energies with an accuracy approaching coupled cluster theory. The performance of DC-SCAN is size-consistent, maintaining an accurate description of molecular interactions well beyond the first solvation shell. Molecular dynamics simulations at ambient conditions with many-body MB-SCAN(DC) potentials, derived from the many-body expansion, predict the solvation structure of Na+ and Cl- in quantitative agreement with reference data, while simultaneously reproducing the structure of liquid water. Beyond rationalizing the accuracy of density-corrected models of ion hydration, our findings suggest that our unified density-corrected MB formalism holds great promise for efficient DFT-based simulations of condensed-phase systems with chemical accuracy.
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Affiliation(s)
- Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Alessandro Caruso
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
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15
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Fuemmeler EG, Damle A, DiStasio RA. Selected Columns of the Density Matrix in an Atomic Orbital Basis I: An Intrinsic and Non-iterative Orbital Localization Scheme for the Occupied Space. J Chem Theory Comput 2023. [PMID: 37944142 DOI: 10.1021/acs.jctc.1c00801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
In this work, we extend the selected columns of the density matrix (SCDM) methodology [J. Chem. Theory Comput. 2015, 11, 1463-1469]─a non-iterative and real-space procedure for generating localized occupied orbitals for condensed-phase systems─to the construction of local molecular orbitals (LMOs) in systems described using non-orthogonal atomic orbital (AO) basis sets. In particular, we introduce three different theoretical and algorithmic variants of SCDM (referred to as SCDM-M, SCDM-L, and SCDM-G) that can be used in conjunction with the AO basis sets used in standard quantum chemistry codebases. The SCDM-M and SCDM-L variants are based on a pivoted QR factorization of the Mulliken and Löwdin representations of the density matrix and are tantamount to selecting a well-conditioned set of projected atomic orbitals (PAOs) and projected (symmetrically-) orthogonalized atomic orbitals, respectively, as proto-LMOs that can be orthogonalized to exactly span the occupied space. The SCDM-G variant is based on a real-space (grid) representation of the wavefunction, and therefore has the added flexibility of considering a large number of grid points (or δ functions) when selecting a set of well-conditioned proto-LMOs. A detailed comparative analysis across molecular systems of varying size, dimensionality, and saturation level reveals that the LMOs generated by these three non-iterative/direct SCDM variants are robust, comparable in orbital locality to those produced with the iterative Boys or Pipek-Mezey (PM) localization schemes, and completely agnostic toward any single orbital locality metric. Although all three SCDM variants are based on the density matrix, we find that the character of the generated LMOs can differ significantly between SCDM-M, SCDM-L, and SCDM-G. In this regard, only the grid-based SCDM-G procedure (like PM) generates LMOs that qualitatively preserve σ-π symmetry (in systems such as s-trans alkenes), and are well-aligned with chemical (i.e., Lewis structure) intuition. While the direct and standalone use of SCDM-generated LMOs should suffice for most chemical applications, our findings also suggest that the use of these orbitals as an unbiased and cost-effective (initial) guess also has the potential to improve the convergence of iterative orbital localization schemes, in particular for large-scale and/or pathological molecular systems.
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Affiliation(s)
- Eric G Fuemmeler
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Anil Damle
- Department of Computer Science, Cornell University, Ithaca, New York 14853, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
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16
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Cinq N, Simon A, Louisnard F, Cuny J. Accurate SCC-DFTB Parametrization of Liquid Water with Improved Atomic Charges and Iterative Boltzmann Inversion. J Phys Chem B 2023; 127:7590-7601. [PMID: 37603798 DOI: 10.1021/acs.jpcb.3c03479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
This work presents improvements of the description of liquid water within the self-consistent-charge density-functional based tight-binding scheme combining the use of Weighted Mulliken (WMull) charges and optimized O-H repulsive potential through the iterative Boltzmann inversion (IBI) process. The quality of the newly developed models is validated considering pair radial distribution functions (RDFs), as well as other structural, energetic, thermodynamic, and dynamic properties. The use of WMull charges certainly improves the agreement with experimental data, however leading to over-structured RDFs at short distance, that can be further improved by considering an optimized O-H repulsive potential obtained by the IBI process. Three different schemes were used to optimize this potential: (i) optimization including short O-H distances. This led to accurate RDFs as well as improved self-diffusion coefficient and heat of vaporization, while the proton transfer energy barrier is severely deteriorated; (ii) optimization starting at long distance. The proton transfer energy barrier is recovered while the heat of vaporization is deteriorated and the O-H RDF is less accurate at short distance; (iii) optimization within the path-integral molecular dynamics scheme which allows us to exclude nuclear quantum effects from the repulsive potential. The latter potential, in conjunction with the WMull improved atomic charges, provides similar results as (i) for structural, dynamic, and thermodynamic properties while recovering a large part of the proton transfer energy barrier. It therefore offers a good compromise to study both dynamic properties and chemistry within liquid water at a quantum chemical level.
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Affiliation(s)
- Nicolas Cinq
- Laboratoire de Chimie et Physique Quantiques (LCPQ), FeRMI Institute, Université de Toulouse [UT3] and CNRS, Toulouse F-31062, France
| | - Aude Simon
- Laboratoire de Chimie et Physique Quantiques (LCPQ), FeRMI Institute, Université de Toulouse [UT3] and CNRS, Toulouse F-31062, France
| | - Fernand Louisnard
- Laboratoire de Chimie et Physique Quantiques (LCPQ), FeRMI Institute, Université de Toulouse [UT3] and CNRS, Toulouse F-31062, France
| | - Jérôme Cuny
- Laboratoire de Chimie et Physique Quantiques (LCPQ), FeRMI Institute, Université de Toulouse [UT3] and CNRS, Toulouse F-31062, France
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17
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Liu R, Chen M. Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics. J Chem Theory Comput 2023; 19:5602-5608. [PMID: 37535904 DOI: 10.1021/acs.jctc.3c00445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with first-principles accuracy. The static structure factors (SSFs) of water at three densities, i.e., 1, 1.115, and 1.24 g/cm3, are directly evaluated from 512 water MD trajectories, which are in quantitative agreement with the experiments. We propose a new method to decompose the computed SSF and identify the changes in the SSF with respect to the changes in H-bond structures. We find that a larger water density results in a higher probability for one or two non-H-bonded water molecules to be inserted into the inner shell, explaining the changes in the tetrahedrality of water under pressure. We predict that the structure of the accepting end of water molecules is more easily influenced by the pressure than by the donating end. Our work sheds new light on explaining the SSF and H-bond properties in related fields.
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Affiliation(s)
- Renxi Liu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 90871, P. R. China
| | - Mohan Chen
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 90871, P. R. China
- AI for Science Institute, Beijing 100080, P. R. China
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18
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Atsango AO, Morawietz T, Marsalek O, Markland TE. Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations. J Chem Phys 2023; 159:074101. [PMID: 37581418 DOI: 10.1063/5.0162066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023] Open
Abstract
The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanisms ideally requires utilizing ab initio molecular dynamics simulations to model the bond breaking and formation involved in proton transfer and path-integral simulations to model the nuclear quantum effects relevant to light hydrogen atoms. These requirements result in a prohibitive computational cost, especially at the time and length scales needed to converge proton transport properties. Here, we present machine-learned potentials (MLPs) that can model both excess protons and hydroxide ions at the generalized gradient approximation and hybrid density functional theory levels of accuracy and use them to perform multiple nanoseconds of both classical and path-integral proton defect simulations at a fraction of the cost of the corresponding ab initio simulations. We show that the MLPs are able to reproduce ab initio trends and converge properties such as the diffusion coefficients of both excess protons and hydroxide ions. We use our multi-nanosecond simulations, which allow us to monitor large numbers of proton transfer events, to analyze the role of hypercoordination in the transport mechanism of the hydroxide ion and provide further evidence for the asymmetry in diffusion between excess protons and hydroxide ions.
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Affiliation(s)
- Austin O Atsango
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Tobias Morawietz
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Ondrej Marsalek
- Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
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19
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Liu R, Zheng D, Liang X, Ren X, Chen M, Li W. Implementation of the meta-GGA exchange-correlation functional in numerical atomic orbital basis: With systematic testing on SCAN, rSCAN, and r2SCAN functionals. J Chem Phys 2023; 159:074109. [PMID: 37602804 DOI: 10.1063/5.0160726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/03/2023] [Indexed: 08/22/2023] Open
Abstract
Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By including the kinetic energy density τ, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. For example, the strongly constrained and appropriately normed (SCAN) meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave bases in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We further test water hexamers, weakly interacting molecules from the S22 dataset, as well as 13 semiconductors using the three functionals. The results show satisfactory agreement with previous calculations and available experimental values.
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Affiliation(s)
- Renxi Liu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 90871, People's Republic of China
- AI for Science Institute, Beijing 100080, People's Republic of China
| | - Daye Zheng
- AI for Science Institute, Beijing 100080, People's Republic of China
| | - Xinyuan Liang
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 90871, People's Republic of China
| | - Xinguo Ren
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People's Republic of China
| | - Mohan Chen
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 90871, People's Republic of China
- AI for Science Institute, Beijing 100080, People's Republic of China
| | - Wenfei Li
- AI for Science Institute, Beijing 100080, People's Republic of China
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20
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Belleflamme F, Hutter J. Radicals in aqueous solution: assessment of density-corrected SCAN functional. Phys Chem Chem Phys 2023; 25:20817-20836. [PMID: 37497572 DOI: 10.1039/d3cp02517a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r2SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r2SCAN maintains the structural accuracy of r2SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH3S∴CH3SH]+ cluster is disrupted in the DC-r2SCAN simulation, in contrast to r2SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH2]+ cluster, r2SCAN restores the hemibonded motif through spin leakage, while DC-r2SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r2SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.
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Affiliation(s)
| | - Jürg Hutter
- Department of Chemistry, University of Zurich, Zurich, Switzerland.
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21
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Chen MS, Lee J, Ye HZ, Berkelbach TC, Reichman DR, Markland TE. Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy. J Chem Theory Comput 2023; 19:4510-4519. [PMID: 36730728 DOI: 10.1021/acs.jctc.2c01203] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Obtaining the atomistic structure and dynamics of disordered condensed-phase systems from first-principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure and provide a data-efficient approach to obtain machine-learned condensed-phase potential energy surfaces using AFQMC, CCSD, and CCSD(T) from a very small number (≤200) of energies by leveraging a transfer learning scheme starting from lower-tier electronic structure methods. We demonstrate the effectiveness of this approach for liquid water by performing both classical and path integral molecular dynamics simulations on these machine-learned potential energy surfaces. By doing this, we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed-phase systems.
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Affiliation(s)
- Michael S Chen
- Department of Chemistry, Stanford University, Stanford, California94305, United States
| | - Joonho Lee
- Department of Chemistry, Columbia University, New York, New York10027, United States
| | - Hong-Zhou Ye
- Department of Chemistry, Columbia University, New York, New York10027, United States
| | - Timothy C Berkelbach
- Department of Chemistry, Columbia University, New York, New York10027, United States
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York10010, United States
| | - David R Reichman
- Department of Chemistry, Columbia University, New York, New York10027, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, California94305, United States
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22
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Sun Q. Exact exchange with range-separated algorithm for thermodynamic limit of periodic Hartree-Fock theory. J Chem Phys 2023; 159:024108. [PMID: 37428044 DOI: 10.1063/5.0155815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
The expensive cost of computing exact exchange in periodic systems limits the application range of density functional theory with hybrid functionals. To reduce the computational cost of exact change, we present a range-separated algorithm to compute electron repulsion integrals for Gaussian-type crystal basis. The algorithm splits the full-range Coulomb interactions into short-range and long-range parts, which are, respectively, computed in real and reciprocal space. This approach significantly reduces the overall computational cost, as integrals can be efficiently computed in both regions. The algorithm can efficiently handle large numbers of k points with limited central processing unit (CPU) and memory resources. As a demonstration, we performed an all-electron k-point Hartree-Fock calculation for LiH crystal with one million Gaussian basis functions, which was completed on a desktop computer in 1400 CPU hours.
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Affiliation(s)
- Qiming Sun
- Quantum Engine LLC, Lacey, Washington 98516, USA
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23
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Ko HY, Calegari Andrade MF, Sparrow ZM, Zhang JA, DiStasio RA. High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach. J Chem Theory Comput 2023. [PMID: 37385014 DOI: 10.1021/acs.jctc.2c00827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx (a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank V^xx operator), and (iii) adaptively compressed exchange (ACE, a low-rank V^xx approximation). In doing so, SeA harnesses three levels of computational savings: pair selection and domain truncation from SCDM + exx (which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and low-rank V^xx approximation from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× compared to the conventional PWSCF(Full) approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ≈8,700 (H2O)64 configurations. Using an out-of-sample set of (H2O)512 configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.
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Affiliation(s)
- Hsin-Yu Ko
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Marcos F Calegari Andrade
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
- Quantum Simulations Group, Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Zachary M Sparrow
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Ju-An Zhang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
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24
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Huo J, Chen J, Liu P, Hong B, Zhang J, Dong H, Li S. Microscopic Mechanism of Proton Transfer in Pure Water under Ambient Conditions. J Chem Theory Comput 2023. [PMID: 37365994 DOI: 10.1021/acs.jctc.3c00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Water molecules and the associated proton transfer (PT) are prevalent in chemical and biological systems and have been a hot research topic. Spectroscopic characterization and ab initio molecular dynamics (AIMD) simulations have previously revealed insights into acidic and basic liquids. Presumably, the situation in the acidic/basic solution is not necessarily the same as in pure water; in addition, the autoionization constant for water is only 10-14 under ambient conditions, making the study of PT in pure water challenging. To overcome this issue, we modeled periodic water box systems containing 1000 molecules for tens of nanoseconds based on a neural network potential (NNP) with quantum mechanical accuracy. The NNP was generated by training a dataset containing the energies and atomic forces of 17 075 configurations of periodic water box systems, and these data points were calculated at the MP2 level that considers electron correlation effects. We found that the size of the system and the duration of the simulation have a significant impact on the convergence of the results. With these factors considered, our simulations showed that hydronium (H3O+) and hydroxide (OH-) ions in water have distinct hydration structures, thermodynamic and kinetic properties, e.g., the longer-lasting and more stable hydrated structure of OH- ions than that of H3O+, as well as a significantly higher free energy barrier for the OH--associated PT than that of H3O+, leading the two to exhibit completely different PT behaviors. Given these characteristics, we further found that PT via OH- ions tends not to occur multiple times or between many molecules. In contrast, PT via H3O+ can synergistically occur among multiple molecules and prefers to adopt a cyclic pattern among three water molecules, while it occurs mostly in a chain pattern when more water molecules are involved. Therefore, our studies provide a detailed and solid microscopic explanation for the PT process in pure water.
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Affiliation(s)
- Jun Huo
- Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China
| | - Jianghao Chen
- Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China
- School of Physics, National Laboratory of Solid State Microstructure, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Pei Liu
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, China
| | - Benkun Hong
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, China
| | - Jian Zhang
- School of Physics, National Laboratory of Solid State Microstructure, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Hao Dong
- Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210023, China
- Institute for Brain Sciences, Nanjing University, Nanjing 210023, China
| | - Shuhua Li
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, China
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25
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The collective burst mechanism of angular jumps in liquid water. Nat Commun 2023; 14:1345. [PMID: 36906703 PMCID: PMC10008639 DOI: 10.1038/s41467-023-37069-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 02/24/2023] [Indexed: 03/13/2023] Open
Abstract
Understanding the microscopic origins of collective reorientational motions in aqueous systems requires techniques that allow us to reach beyond our chemical imagination. Herein, we elucidate a mechanism using a protocol that automatically detects abrupt motions in reorientational dynamics, showing that large angular jumps in liquid water involve highly cooperative orchestrated motions. Our automatized detection of angular fluctuations, unravels a heterogeneity in the type of angular jumps occurring concertedly in the system. We show that large orientational motions require a highly collective dynamical process involving correlated motion of many water molecules in the hydrogen-bond network that form spatially connected clusters going beyond the local angular jump mechanism. This phenomenon is rooted in the collective fluctuations of the network topology which results in the creation of defects in waves on the THz timescale. The mechanism we propose involves a cascade of hydrogen-bond fluctuations underlying angular jumps and provides new insights into the current localized picture of angular jumps, and its wide use in the interpretations of numerous spectroscopies as well in reorientational dynamics of water near biological and inorganic systems. The role of finite size effects, as well as of the chosen water model, on the collective reorientation is also elucidated.
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26
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Schackert F, Biedermann J, Abdolvand S, Minniberger S, Song C, Plested AJR, Carloni P, Sun H. Mechanism of Calcium Permeation in a Glutamate Receptor Ion Channel. J Chem Inf Model 2023; 63:1293-1300. [PMID: 36758214 PMCID: PMC9976283 DOI: 10.1021/acs.jcim.2c01494] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) are neurotransmitter-activated cation channels ubiquitously expressed in vertebrate brains. The regulation of calcium flux through the channel pore by RNA-editing is linked to synaptic plasticity while excessive calcium influx poses a risk for neurodegeneration. Unfortunately, the molecular mechanisms underlying this key process are mostly unknown. Here, we investigated calcium conduction in calcium-permeable AMPAR using Molecular Dynamics (MD) simulations with recently introduced multisite force-field parameters for Ca2+. Our calculations are consistent with experiment and explain the distinct calcium permeability in different RNA-edited forms of GluA2. For one of the identified metal binding sites, multiscale Quantum Mechanics/Molecular Mechanics (QM/MM) simulations further validated the results from MD and revealed small but reproducible charge transfer between the metal ion and its first solvation shell. In addition, the ion occupancy derived from MD simulations independently reproduced the Ca2+ binding profile in an X-ray structure of an NaK channel mimicking the AMPAR selectivity filter. This integrated study comprising X-ray crystallography, multisite MD, and multiscale QM/MM simulations provides unprecedented insights into Ca2+ permeation mechanisms in AMPARs, and paves the way for studying other biological processes in which Ca2+ plays a pivotal role.
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Affiliation(s)
- Florian
Karl Schackert
- Computational
Biomedicine (IAS-5/INM-9), Forschungszentrum
Jülich GmbH, 52428 Jülich, Germany,Department
of Physics, RWTH Aachen University, 52062 Aachen, Germany
| | - Johann Biedermann
- Institute
of Biology, Cellular Biophysics, Humboldt
Universität zu Berlin, 10115 Berlin, Germany,Leibniz
Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Saeid Abdolvand
- Institute
of Biology, Cellular Biophysics, Humboldt
Universität zu Berlin, 10115 Berlin, Germany,Leibniz
Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Sonja Minniberger
- Institute
of Biology, Cellular Biophysics, Humboldt
Universität zu Berlin, 10115 Berlin, Germany,Leibniz
Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Chen Song
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China,Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Andrew J. R. Plested
- Institute
of Biology, Cellular Biophysics, Humboldt
Universität zu Berlin, 10115 Berlin, Germany,Leibniz
Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Paolo Carloni
- Computational
Biomedicine (IAS-5/INM-9), Forschungszentrum
Jülich GmbH, 52428 Jülich, Germany,Department
of Physics, RWTH Aachen University, 52062 Aachen, Germany,
| | - Han Sun
- Leibniz
Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany,Institute
of Chemistry, TU Berlin, Straße des 17 Juni 135, 10623 Berlin, Germany,
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27
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Li W, Ou Q, Chen Y, Cao Y, Liu R, Zhang C, Zheng D, Cai C, Wu X, Wang H, Chen M, Zhang L. DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials. J Phys Chem A 2022; 126:9154-9164. [DOI: 10.1021/acs.jpca.2c05000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Wenfei Li
- AI for Science Institute, Beijing100080, P. R. China
| | - Qi Ou
- AI for Science Institute, Beijing100080, P. R. China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey08544, United States
| | - Yu Cao
- HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing100871, P. R. China
| | - Renxi Liu
- HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing100871, P. R. China
| | - Chunyi Zhang
- Department of Physics, Temple University, Philadelphia, Pennsylvania19122, United States
| | - Daye Zheng
- AI for Science Institute, Beijing100080, P. R. China
| | - Chun Cai
- AI for Science Institute, Beijing100080, P. R. China
- DP Technology, Beijing100080, P. R. China
| | - Xifan Wu
- Department of Physics, Temple University, Philadelphia, Pennsylvania19122, United States
| | - Han Wang
- Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Huayuan Road 6, Beijing100088, P. R. China
| | - Mohan Chen
- HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing100871, P. R. China
| | - Linfeng Zhang
- AI for Science Institute, Beijing100080, P. R. China
- DP Technology, Beijing100080, P. R. China
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28
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Li Y, Li S, Cui J, Yan J, Tan HH, Liu J, Wu Y. TiO 2 nanotubular arrays decorated with ultrafine Ag nanoseeds enabling a stable and dendrite-free lithium metal anode. NANOSCALE ADVANCES 2022; 4:4639-4647. [PMID: 36341294 PMCID: PMC9595180 DOI: 10.1039/d2na00526c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
To exploit next-generation high-energy Li metal batteries, it is vitally important to settle the issue of dendrite growth accompanied by interfacial instability of the Li anode. Applying 3D current collectors as hosts for Li deposition emerges as a prospective strategy to achieve uniform Li nucleation and suppress Li dendrites. Herein, well-aligned and spaced TiO2 nanotube arrays grown on Ti foil and surface decorated with dispersed Ag nanocrystals (Ag@TNTAs/Ti) were constructed and employed as a 3D host for regulating Li stripping/plating behaviors and suppressing Li dendrites, and also relieving volume fluctuation during repetitive Li plating/stripping. Uniform TiO2 nanotubular structures with a large surface allow fast electron/ion transport and uniform local current density distribution, leading to homogeneous Li growth on the nanotube surface. Moreover, Ag nanocrystals and TiO2 nanotubes have good Li affinity, which facilitates Li+ capture and reduces the Li nucleation barrier, achieving uniform nucleation and growth of Li metal over the 3D Ag@TNTAs/Ti host. As a result, the as-fabricated Ag@TNTAs/Ti electrode exhibits dendrite-free plating morphology and long-term cycle stability with coulombic efficiency maintained over 98.5% even after 1000 cycles at a current density of 1 mA cm-2 and cycling capacity of 1 mA h cm-2. In symmetric cells, the Ag@TNTAs/Ti-Li electrode shows a much lower hysteresis of 40 mV over an ultralong cycle period of 2600 h at a current density of 1 mA cm-2 and cycling capacity of 1 mA h cm-2. Moreover, the full cell with the Ag@TNTAs/Ti-Li anode and LiFePO4 cathode achieves a high capacity of 155.2 mA h g-1 at 0.5C and retains 77.9% capacity with an average CE of ≈99.7% over 200 cycles.
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Affiliation(s)
- Yulei Li
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
| | - Shenhao Li
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
| | - Jiewu Cui
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
| | - Jian Yan
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
| | - Hark Hoe Tan
- Department of Electronic Materials Engineering, Research School of Physics and Engineering, Australian National University Canberra ACT 2601 Australia
| | - Jiaqin Liu
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
| | - Yucheng Wu
- Institute of Industry & Equipment Technology, School of Materials Science and Engineering, Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province, Key Laboratory of Advanced Functional Materials & Devices of Anhui Province, Hefei University of Technology Hefei 230009 China
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29
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Lu D, Jiang W, Chen Y, Zhang L, Jia W, Wang H, Chen M. DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models. J Chem Theory Comput 2022; 18:5559-5567. [PMID: 35926122 DOI: 10.1021/acs.jctc.2c00102] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning-based PES model. This scheme, we call DP Compress, is an efficient postprocessing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online.
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Affiliation(s)
- Denghui Lu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China
| | - Wanrun Jiang
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, P. R. China.,Institute of Physics, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, United States
| | - Linfeng Zhang
- Beijing Institute of Big Data Research, Beijing 100871, P. R. China
| | - Weile Jia
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Han Wang
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China.,Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, P. R. China
| | - Mohan Chen
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China
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30
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Muñoz-Santiburcio D. Accurate diffusion coefficients of the excess proton and hydroxide in water via extensive ab initio simulations with different schemes. J Chem Phys 2022; 157:024504. [PMID: 35840376 DOI: 10.1063/5.0093958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Despite its simple molecular formula, obtaining an accurate in silico description of water is far from straightforward. Many of its very peculiar properties are quite elusive, and in particular, obtaining good estimations of the diffusion coefficients of the solvated proton and hydroxide at a reasonable computational cost has been an unsolved challenge until now. Here, I present extensive results of several unusually long ab initio molecular dynamics (MD) simulations employing different combinations of the Born-Oppenheimer and second-generation Car-Parrinello MD propagation methods with different ensembles (NVE and NVT) and thermostats, which show that these methods together with the RPBE-D3 functional provide a very accurate estimation of the diffusion coefficients of the solvated H3O+ and OH- ions, together with an extremely accurate description of several properties of neutral water (such as the structure of the liquid and its diffusion and shear viscosity coefficients). In addition, I show that the estimations of DH3O+ and DOH- depend dramatically on the simulation length, being necessary to reach timescales in the order of hundreds of picoseconds to obtain reliable results.
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Affiliation(s)
- Daniel Muñoz-Santiburcio
- CIC nanoGUNE BRTA, Tolosa Hiribidea 76, 20018 San Sebastián, Spain and Instituto de Fusión Nuclear "Guillermo Velarde," Universidad Politécnica de Madrid, C/ José Gutiérrez Abascal 2, 28006 Madrid, Spain
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31
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Liu J, Lan J, He X. Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks. J Phys Chem A 2022; 126:3926-3936. [PMID: 35679610 DOI: 10.1021/acs.jpca.2c00601] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accurate and efficient simulation of liquids, such as water and salt solutions, using high-level wave function theories is still a formidable task for computational chemists owing to the high computational costs. In this study, we develop a deep machine learning potential based on fragment-based second-order Møller-Plesset perturbation theory (DP-MP2) for water through neural networks. We show that the DP-MP2 potential predicts the structural, dynamical, and thermodynamic properties of liquid water in better agreement with the experimental data than previous studies based on density functional theory (DFT). The nuclear quantum effects (NQEs) on the properties of liquid water are also examined, which are noticeable in affecting the structural and dynamical properties of liquid water under ambient conditions. This work provides a general framework for quantitative predictions of the properties of condensed-phase systems with the accuracy of high-level wave function theory while achieving significant computational savings compared to ab initio simulations.
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Affiliation(s)
- Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China.,Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Jinggang Lan
- Chaire de Simulation à l'Echelle Atomique (CSEA), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,New York University-East China Normal University Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China
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32
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Liu R, Zhang C, Liang X, Liu J, Wu X, Chen M. Structural and Dynamic Properties of Solvated Hydroxide and Hydronium Ions in Water from Ab Initio Modeling. J Chem Phys 2022; 157:024503. [DOI: 10.1063/5.0094944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water has been a challenging task from ab initio molecular dynamics (AIMD). The difficulty mainly comes from a lack of accurate and efficient exchange-correlation functional in elucidating the amphiphilic nature and the ubiquitous proton transfer behaviors of the two ions. By adopting the strongly-constrained and appropriately normed (SCAN) meta-GGA functional in AIMD simulations, we systematically examine the amphiphilic properties, the solvation structures, the electronic structures, and the dynamic properties of the two water ions. In particular, we compare these results to those predicted by the PBE0-TS functional, which is an accurate yet computationally more expensive exchange-correlation functional. We demonstrate that the general-purpose SCAN functional provides a reliable choice in describing the two water ions. Specifically, in the SCAN picture of water ions, the appearance of the fourth and fifth hydrogen bonds near hydroxide stabilizes the pot-like shape solvation structure and suppresses the structural diffusion, while the hydronium stably donates three hydrogen bonds to its neighbors. We apply a detailed analysis of the proton transfer mechanism of the two ions and find the two ions exhibit substantially different proton transfer patterns. Our AIMD simulations indicate hydroxide diffuses slower than hydronium in water, which is consistent with the experiments.
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Affiliation(s)
| | | | | | | | - Xifan Wu
- Physics, Temple University, United States of America
| | - Mohan Chen
- College of Engineering, Peking University, China
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33
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Martelli F. Steady-like topology of the dynamical hydrogen bond network in supercooled water. PNAS NEXUS 2022; 1:pgac090. [PMID: 36741425 PMCID: PMC9896910 DOI: 10.1093/pnasnexus/pgac090] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/08/2022] [Indexed: 02/07/2023]
Abstract
We investigate the link between topology of the hydrogen bond network (HBN) and large-scale density fluctuations in water from ambient conditions to the glassy state. We observe a transition from a temperature-dependent topology at high temperatures, to a steady-like topology below the Widom temperature TW ∼ 220 K signaling the fragile-to-strong crossover and the maximum in structural fluctuations. As a consequence of the steady topology, the network suppresses large-scale density fluctuations much more efficiently than at higher temperatures. Below TW , the contribution of coordination defects of the kind A 2 D 1 (two acceptors and one donor) to the kinetics of the HBN becomes progressively more pronounced, suggesting that A 2 D 1 configurations may represent the main source of dynamical heterogeneities. Below the vitrification temperature, the freezing of rotational and translational degrees of freedom allow for an enhanced suppression of large-scale density fluctuations and the sample reaches the edges of nearly hyperuniformity. The formed network still hosts coordination defects, hence implying that nearly hyperuniformity goes beyond the classical continuous random network paradigm of tetrahedral networks and can emerge in scenarios much more complex than previously assumed. Our results unveil a hitherto undisclosed link between network topology and properties of water essential for better understanding water's rich and complex nature. Beyond implications for water, our findings pave the way to a better understanding of the physics of supercooled liquids and disordered hyperuniform networks at large.
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34
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Tang F, Li Z, Zhang C, Louie SG, Car R, Qiu DY, Wu X. Many-body effects in the X-ray absorption spectra of liquid water. Proc Natl Acad Sci U S A 2022; 119:e2201258119. [PMID: 35561212 PMCID: PMC9171919 DOI: 10.1073/pnas.2201258119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/18/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceIn X-ray absorption spectroscopy, an electron-hole excitation probes the local atomic environment. The interpretation of the spectra requires challenging theoretical calculations, particularly in a system like liquid water, where quantum many-body effects and molecular disorder play an important role. Recent advances in theory and simulation make possible new calculations that are in good agreement with experiment, without recourse to commonly adopted approximations. Based on these calculations, the three features observed in the experimental spectra are unambiguously attributed to excitonic effects with different characteristic correlation lengths, which are distinctively affected by perturbations of the underlying H-bond structure induced by temperature changes and/or by isotopic substitution. The emerging picture of the water structure is fully consistent with the conventional tetrahedral model.
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Affiliation(s)
- Fujie Tang
- Department of Physics, Temple University, Philadelphia, PA 19122
| | - Zhenglu Li
- Department of Physics, University of California, Berkeley, CA 94720
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
| | - Chunyi Zhang
- Department of Physics, Temple University, Philadelphia, PA 19122
| | - Steven G. Louie
- Department of Physics, University of California, Berkeley, CA 94720
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
| | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, NJ 08544
| | - Diana Y. Qiu
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT 06520
| | - Xifan Wu
- Department of Physics, Temple University, Philadelphia, PA 19122
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35
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Self-consistent determination of long-range electrostatics in neural network potentials. Nat Commun 2022; 13:1572. [PMID: 35322046 PMCID: PMC8943018 DOI: 10.1038/s41467-022-29243-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/07/2022] [Indexed: 12/19/2022] Open
Abstract
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network — a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions — and demonstrate its utility by modeling liquid water with and without applied fields. Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the electronic and nuclear response of molecular systems to long-range electrostatics.
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36
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Zaverkin V, Holzmüller D, Schuldt R, Kästner J. Predicting properties of periodic systems from cluster data: A case study of liquid water. J Chem Phys 2022; 156:114103. [DOI: 10.1063/5.0078983] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out of scope for periodic structures. We show that local, atom-centered descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental and theoretical counterparts.
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Affiliation(s)
- Viktor Zaverkin
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| | - David Holzmüller
- Institute for Stochastics and Applications, University of Stuttgart, Pfaffenwaldring 57, 70569 Stuttgart, Germany
| | - Robin Schuldt
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| | - Johannes Kästner
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
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37
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Prasad VK, Otero-de-la-Roza A, DiLabio GA. Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials. J Chem Theory Comput 2022; 18:2208-2232. [PMID: 35313106 DOI: 10.1021/acs.jctc.1c01128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.
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Affiliation(s)
- Viki Kumar Prasad
- Department of Chemistry, University of British Columbia, Okanagan, 3247 University Way, Kelowna, British Columbia, Canada V1V 1V7
| | - Alberto Otero-de-la-Roza
- MALTA Consolider Team, Departamento de Química Física y Analítica, Facultad de Química, Universidad de Oviedo, E-33006 Oviedo, Spain
| | - Gino A DiLabio
- Department of Chemistry, University of British Columbia, Okanagan, 3247 University Way, Kelowna, British Columbia, Canada V1V 1V7
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38
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Muthachikavil AV, Kontogeorgis GM, Liang X, Lei Q, Peng B. Structural characteristics of low-density environments in liquid water. Phys Rev E 2022; 105:034604. [PMID: 35428046 DOI: 10.1103/physreve.105.034604] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
The existence of two structural forms in liquid water has been a point of discussion for a long time. A phase transition between these two forms of liquid water has been proposed based on evidence from molecular simulations, and experiments have also been very recently able to track the proposed transition of the low-density liquid form to the high-density liquid form. We propose to use the average angle an oxygen atom makes with its neighbors to describe the structural environment of a water molecule. The distribution of this order parameter is observed to have two peaks with one peak at ∼109.5^{∘}, corresponding to the internal angle of a regular tetrahedron, indicating tetrahedral arrangement. The other peak corresponds to an environment with a tighter arrangement of neighboring molecules. The distribution of O-O-O angles is decomposed into two skewed distributions to estimate the fractions of the two liquid forms in water. A good similarity is observed between the temperature and pressure trends of fractions of locally favored tetrahedral structure (LFTS) form estimated using the new order parameter and the reports in the literature, over a range of temperatures and pressures. We also compare the structural environments indicated by different order parameters and find that the order parameter proposed in this paper captures the structure of first solvation shell of the LFTS accurately.
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Affiliation(s)
- Aswin V Muthachikavil
- Department of Chemical and Biochemical Engineering, Center for Energy Resources Engineering, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Georgios M Kontogeorgis
- Department of Chemical and Biochemical Engineering, Center for Energy Resources Engineering, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Xiaodong Liang
- Department of Chemical and Biochemical Engineering, Center for Energy Resources Engineering, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Qun Lei
- Research Institute of Petroleum Exploration and Development (RIPED), PetroChina, Beijing 100083, China
| | - Baoliang Peng
- Research Institute of Petroleum Exploration and Development (RIPED), PetroChina, Beijing 100083, China
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39
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Mandal S, Kar R, Klöffel T, Meyer B, Nair NN. Improving the scaling and performance of multiple time stepping-based molecular dynamics with hybrid density functionals. J Comput Chem 2022; 43:588-597. [PMID: 35147988 DOI: 10.1002/jcc.26816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/07/2022] [Accepted: 01/18/2022] [Indexed: 12/18/2022]
Abstract
Density functionals at the level of the generalized gradient approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (second rung) to hybrid density functionals (fourth rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method and proposes a way to overcome it.
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Affiliation(s)
- Sagarmoy Mandal
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India.,Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ritama Kar
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Tobias Klöffel
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bernd Meyer
- Interdisciplinary Center for Molecular Materials and Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
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40
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Yang Y, Peltier CR, Zeng R, Schimmenti R, Li Q, Huang X, Yan Z, Potsi G, Selhorst R, Lu X, Xu W, Tader M, Soudackov AV, Zhang H, Krumov M, Murray E, Xu P, Hitt J, Xu L, Ko HY, Ernst BG, Bundschu C, Luo A, Markovich D, Hu M, He C, Wang H, Fang J, DiStasio RA, Kourkoutis LF, Singer A, Noonan KJT, Xiao L, Zhuang L, Pivovar BS, Zelenay P, Herrero E, Feliu JM, Suntivich J, Giannelis EP, Hammes-Schiffer S, Arias T, Mavrikakis M, Mallouk TE, Brock JD, Muller DA, DiSalvo FJ, Coates GW, Abruña HD. Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies. Chem Rev 2022; 122:6117-6321. [PMID: 35133808 DOI: 10.1021/acs.chemrev.1c00331] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.
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Affiliation(s)
- Yao Yang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Cheyenne R Peltier
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Rui Zeng
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Roberto Schimmenti
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Qihao Li
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Xin Huang
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Zhifei Yan
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Georgia Potsi
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Ryan Selhorst
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Xinyao Lu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Weixuan Xu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Mariel Tader
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Alexander V Soudackov
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Hanguang Zhang
- Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Mihail Krumov
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Ellen Murray
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Pengtao Xu
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Jeremy Hitt
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Linxi Xu
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Hsin-Yu Ko
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Brian G Ernst
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Colin Bundschu
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Aileen Luo
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Danielle Markovich
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Meixue Hu
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Cheng He
- Chemical and Materials Science Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Hongsen Wang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Jiye Fang
- Department of Chemistry, State University of New York at Binghamton, Binghamton, New York 13902, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Lena F Kourkoutis
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Andrej Singer
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Kevin J T Noonan
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Li Xiao
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Lin Zhuang
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Bryan S Pivovar
- Chemical and Materials Science Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Piotr Zelenay
- Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Enrique Herrero
- Instituto de Electroquímica, Universidad de Alicante, Alicante E-03080, Spain
| | - Juan M Feliu
- Instituto de Electroquímica, Universidad de Alicante, Alicante E-03080, Spain
| | - Jin Suntivich
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Emmanuel P Giannelis
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | | | - Tomás Arias
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Thomas E Mallouk
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Joel D Brock
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - David A Muller
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States.,Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, United States
| | - Francis J DiSalvo
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Geoffrey W Coates
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Héctor D Abruña
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Center for Alkaline Based Energy Solutions (CABES), Cornell University, Ithaca, New York 14853, United States
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41
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Kanungo B, Zimmerman PM, Gavini V. A Comparison of Exact and Model Exchange-Correlation Potentials for Molecules. J Phys Chem Lett 2021; 12:12012-12019. [PMID: 34898217 DOI: 10.1021/acs.jpclett.1c03670] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Accurate exchange-correlation (XC) potentials for three-dimensional systems─via solution of the inverse density functional theory (DFT) problem─are now available to test the quality of DFT approximations. Herein, the exact XC potential for seven molecules─dihydrogen at four different bond-lengths, lithium hydride, water, and ortho-benzyne─are computed from full configuration interaction reference densities. These are compared to model XC potentials from nonlocal (B3LYP, HSE06, SCAN0, and M08-HX) and semilocal/local (SCAN, PBE, and PW92) XC functionals. Whereas for most systems, relative errors in the ground-state densities are O(10-3-10-2), the model XC potentials have much higher errors of O(10-1-100). Among the model XC functionals, SCAN0 offers the best agreement with the exact XC potential, underlining the significance of satisfying exact conditions as well as including nonlocal effects in XC functionals. This work indicates that tests against the exact XC potential will provide a promising new direction for building more accurate XC functionals for DFT.
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Affiliation(s)
- Bikash Kanungo
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Vikram Gavini
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
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42
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Abdul Nasir J, Munir A, Ahmad N, Haq TU, Khan Z, Rehman Z. Photocatalytic Z-Scheme Overall Water Splitting: Recent Advances in Theory and Experiments. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2105195. [PMID: 34617345 DOI: 10.1002/adma.202105195] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Photocatalytic water splitting is considered one of the most important and appealing approaches for the production of green H2 to address the global energy demand. The utmost possible form of artificial photosynthesis is a two-step photoexcitation known as "Z-scheme", which mimics the natural photosystem. This process solely relies on the effective coupling and suitable band positions of semiconductors (SCs) and redox mediators for the purpose to catalyze the surface chemical reactions and significantly deter the backward reaction. In recent years, the Z-scheme strategies and their key role have been studied progressively through experimental approaches. In addition, theoretical studies based on density functional theory have provided detailed insight into the mechanistic aspects of some breathtakingly complex problems associated with hydrogen evolution reaction and oxygen evolution reaction. In this context, this critical review gives an overview of the fundamentals of Z-scheme photocatalysis, including both theoretical and experimental advancements in the field of photocatalytic water splitting, and suggests future perspectives.
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Affiliation(s)
- Jamal Abdul Nasir
- Kathleen Lonsdale Materials Chemistry, Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, UK
- Department of Chemistry, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Akhtar Munir
- Department of Chemistry, University of Sialkot, 1 Km, main Daska road, Sialkot, Punjab, 51310, Pakistan
- Department of Chemistry & Chemical Engineering, SBA School of Science & Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore, 54792, Pakistan
| | - Naveed Ahmad
- Institute of Pharmaceutical Science, Faculty of Life Science and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK
- University of Swat. Charbagh, Swat, Khyber Pakhtunkhwa, Pakistan
| | - Tanveer Ul Haq
- Sustainable Energy Engineering, Frank H. Dotterweich College of Engineering, Texas A&M University, Kingsville, TX, 78363-8202, USA
| | - Zaibunisa Khan
- Kathleen Lonsdale Materials Chemistry, Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, UK
| | - Ziaur Rehman
- Kathleen Lonsdale Materials Chemistry, Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, UK
- Department of Chemistry, Quaid-i-Azam University, Islamabad, 45320, Pakistan
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43
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Liu J, He X. Ab initio molecular dynamics simulation of liquid water with fragment-based quantum mechanical approach under periodic boundary conditions. CHINESE J CHEM PHYS 2021. [DOI: 10.1063/1674-0068/cjcp2110183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- New York University-East China Normal University Center for Computational Chemistry at New York University Shanghai, Shanghai 200062, China
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44
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Thaler S, Zavadlav J. Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. Nat Commun 2021; 12:6884. [PMID: 34824254 PMCID: PMC8617111 DOI: 10.1038/s41467-021-27241-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/09/2021] [Indexed: 11/09/2022] Open
Abstract
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.
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Affiliation(s)
- Stephan Thaler
- Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.
| | - Julija Zavadlav
- Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.
- Munich Data Science Institute, Technical University of Munich, Munich, Germany.
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45
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Ko HY, Santra B, DiStasio RA. Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory-Based Ab Initio Molecular Dynamics II: Extensions to the Isobaric-Isoenthalpic and Isobaric-Isothermal Ensembles. J Chem Theory Comput 2021; 17:7789-7813. [PMID: 34775753 DOI: 10.1021/acs.jctc.0c01194] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In the previous paper of this series [Ko, H.-Y. et al. J. Chem. Theory Comput. 2020, 16, 3757-3785], we presented a theoretical and algorithmic framework based on a localized representation of the occupied space that exploits the inherent sparsity in the real-space evaluation of the exact exchange (EXX) interaction in finite-gap systems. This was accompanied by a detailed description of exx, a massively parallel hybrid message-passing interface MPI/OpenMP implementation of this approach in Quantum ESPRESSO (QE) that enables linear scaling hybrid density functional theory (DFT)-based ab initio molecular dynamics (AIMD) in the microcanonical/canonical (NVE/NVT) ensembles of condensed-phase systems containing 500-1000 atoms (in fixed orthorhombic cells) with a wall time cost comparable to semi-local DFT. In this work, we extend the current capabilities of exx to enable hybrid DFT-based AIMD simulations of large-scale condensed-phase systems with general and fluctuating cells in the isobaric-isoenthalpic/isobaric-isothermal (NpH/NpT) ensembles. The theoretical extensions to this approach include an analytical derivation of the EXX contribution to the stress tensor for systems in general simulation cells with a computational complexity that scales linearly with system size. The corresponding algorithmic extensions to exx include optimized routines that (i) handle both static and fluctuating simulation cells with non-orthogonal lattice symmetries, (ii) solve Poisson's equation in general/non-orthogonal cells via an automated selection of the auxiliary grid directions in the Natan-Kronik representation of the discrete Laplacian operator, and (iii) evaluate the EXX contribution to the stress tensor. Using this approach, we perform a case study on a variety of condensed-phase systems (including liquid water, a benzene molecular crystal polymorph, and semi-conducting crystalline silicon) and demonstrate that the EXX contributions to the energy and stress tensor simultaneously converge with an appropriate choice of exx parameters. This is followed by a critical assessment of the computational performance of the extended exx module across several different high-performance computing architectures via case studies on (i) the computational complexity due to lattice symmetry during NpT simulations of three different ice polymorphs (i.e., ice Ih, II, and III) and (ii) the strong/weak parallel scaling during large-scale NpT simulations of liquid water. We demonstrate that the robust and highly scalable implementation of this approach in the extended exx module is capable of evaluating the EXX contribution to the stress tensor with negligible cost (<1%) as well as all other EXX-related quantities needed during NpT simulations of liquid water (with a very tight 150 Ry planewave cutoff) in ≈5.2 s ((H2O)128) and ≈6.8 s ((H2O)256) per AIMD step. As such, the extended exx module presented in this work brings us another step closer to routinely performing hybrid DFT-based AIMD simulations of sufficient duration for large-scale condensed-phase systems across a wide range of thermodynamic conditions.
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Affiliation(s)
- Hsin-Yu Ko
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Biswajit Santra
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Robert A DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
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46
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Zhang C, Tang F, Chen M, Xu J, Zhang L, Qiu DY, Perdew JP, Klein ML, Wu X. Modeling Liquid Water by Climbing up Jacob's Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials. J Phys Chem B 2021; 125:11444-11456. [PMID: 34533960 DOI: 10.1021/acs.jpcb.1c03884] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob's ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric-isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.
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Affiliation(s)
- Chunyi Zhang
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Fujie Tang
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Mohan Chen
- HEDPS, Center for Applied Physics and Technology, College of Engineering, Peking University, Beijing 100871, China
| | - Jianhang Xu
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Linfeng Zhang
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, United States
| | - Diana Y Qiu
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, Connecticut 06520, United States
| | - John P Perdew
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States.,Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Michael L Klein
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States.,Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Xifan Wu
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
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Machine learning potentials for complex aqueous systems made simple. Proc Natl Acad Sci U S A 2021; 118:2110077118. [PMID: 34518232 PMCID: PMC8463804 DOI: 10.1073/pnas.2110077118] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2021] [Indexed: 12/23/2022] Open
Abstract
Understanding complex materials, in particular those with solid–liquid interfaces, such as water on surfaces or under confinement, is a key challenge for technological and scientific progress. Although established simulation approaches have been able to provide important atomistic insight, ab initio techniques struggle with the required time and length scales, while force field methods can often be limited in terms of their accuracy. Here we show how these limitations can be overcome in a simple and automated machine learning procedure to provide accurate models of interactions at the ab initio level, as illustrated for a variety of complex aqueous systems. These developments open up the prospect of the straightforward exploration of many technologically relevant systems by molecular simulations. Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.
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48
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Batista PR, Penna TC, Ducati LC, Correra TC. p-Aminobenzoic acid protonation dynamics in an evaporating droplet by ab initio molecular dynamics. Phys Chem Chem Phys 2021; 23:19659-19672. [PMID: 34524295 DOI: 10.1039/d1cp01495a] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Protonation equilibria are known to vary from the bulk to microdroplet conditions, which could induce many chemical and physical phenomena. Protonated p-aminobenzoic acid (PABA + H+) can be considered a model for probing the protonation dynamics in an evaporating droplet, as its protonation equilibrium is highly dependent on the formation conditions from solution via atmospheric pressure ionization sources. Experiments using diverse experimental techniques have shown that protic solvents allow formation of the O-protomer (PABA protonated in the carboxylic acid group) stable in the gas phase, while aprotic solvents yield the N-protomer (protonated in the amino group) that is the most stable protomer in solution. In this work, we explore the protonation equilibrium of PABA solvated by different numbers of water molecules (n = 0 to 32) using ab initio molecular dynamics. For n = 8-32, the protonation is either at the NH2 group or in the solvent network. The solvent network interacts with the carboxylic acid group, but there is no complete proton transfer to form the O-protomer. For smaller clusters, however, solvent-mediated proton transfers to the carboxylic acid were observed, both via the Grotthuss mechanism and the vehicle or shuttle mechanism (for n = 1 and 2). Thermodynamic considerations allowed a description of the origins of the kinetic trapping effect, which explains the observation of the solution structure in the gas phase. This effect likely occurs in the final evaporation steps, which are outside the droplet size range covered by previous classical molecular dynamics simulations of charged droplets. These results may be considered relevant in determining the nature of the species observed in the ubiquitous ESI based mass spectrometry analysis, and in general for droplet chemistry, explaining how protonation equilibria are drastically changed from bulk to microdroplet conditions.
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Affiliation(s)
- Patrick R Batista
- Department of Fundamental Chemistry, Institute of Chemistry - University of São Paulo, Av. Prof. Lineu Prestes, 748, Cidade Universitária, São Paulo, SP, Brazil.
| | - Tatiana C Penna
- Department of Fundamental Chemistry, Institute of Chemistry - University of São Paulo, Av. Prof. Lineu Prestes, 748, Cidade Universitária, São Paulo, SP, Brazil.
| | - Lucas C Ducati
- Department of Fundamental Chemistry, Institute of Chemistry - University of São Paulo, Av. Prof. Lineu Prestes, 748, Cidade Universitária, São Paulo, SP, Brazil.
| | - Thiago C Correra
- Department of Fundamental Chemistry, Institute of Chemistry - University of São Paulo, Av. Prof. Lineu Prestes, 748, Cidade Universitária, São Paulo, SP, Brazil.
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Chedid J, Jocelyn N, Eshuis H. Energies, structures, and harmonic frequencies of small water clusters from the direct random phase approximation. J Chem Phys 2021; 155:084303. [PMID: 34470345 DOI: 10.1063/5.0059343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The binding energies, structures, and vibrational frequencies of water clusters up to 20 molecules are computed at the direct random phase approximation (RPA) level of theory and compared to theoretical benchmarks. Binding energies of the WATER27 set, which includes neutral and positively and negatively charged clusters, are predicted to be too low in the complete basis set limit by an average of 7 kcal/mol (9%) and are worse than the results from the best density functional theory methods or from the Møller-Plesset theory. The RPA shows significant basis set size dependence for binding energies. The order of the relative energies of the water hexamer and dodecamer isomers is predicted correctly by the RPA. The mean absolute deviation for angles and distances for neutral clusters up to the water hexamer are 0.2° and 0.6 pm, respectively, using quintuple-ζ basis sets. The relative energetic order of the hexamer isomers is preserved upon optimization. Vibrational frequencies for these systems are underestimated by several tens of wavenumbers for large basis sets, and deviations increase with the basis set size. Overall, the direct RPA method yields accurate structural parameters but systematically underestimates binding energies and shows strong basis set size dependence.
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Affiliation(s)
- Julianna Chedid
- Department of Chemistry and Biochemistry, Montclair State University, Montclair, New Jersey 07043, USA
| | - Nedjie Jocelyn
- Department of Chemistry and Biochemistry, Montclair State University, Montclair, New Jersey 07043, USA
| | - Henk Eshuis
- Department of Chemistry and Biochemistry, Montclair State University, Montclair, New Jersey 07043, USA
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50
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Batista PR, Ducati LC, Autschbach J. Solvent effect on the 195Pt NMR properties in pyridonate-bridged Pt III dinuclear complex derivatives investigated by ab initio molecular dynamics and localized orbital analysis. Phys Chem Chem Phys 2021; 23:12864-12880. [PMID: 34075921 DOI: 10.1039/d0cp05849a] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
An ab initio molecular dynamics investigation of the solvent effect (water) on the structural parameters, 195Pt NMR spin-spin coupling constants (SSCCs) and chemical shifts of a series of pyridonate-bridged PtIII dinuclear complexes is performed using Kohn-Sham (KS) Car-Parrinello molecular dynamics (CPMD) and relativistic hybrid KS NMR calculations. The indirect solvent effect (via structural changes) has a dramatic effect on the 1JPtPt SSCCs. The complexes exhibit a strong trans influence in solution, where the Pt-Pt bond lengthens with increasing axial ligand σ-donor strength. In the diaqua complex, where the solvent effect is more pronounced, the SSCCs averaged for CPMD configurations with explicit plus implicit solvation agree much better with the experimental data, while the calculations for static geometry and CPMD unsolvated configurations show large deviations with respect to experiment. The combination of CPMD with hybrid KS NMR calculations provides a much more realistic computational model that reproduces the large magnitudes of 1JPtPt and 195Pt chemical shifts. An analysis of 1JPtPt in terms of localized and canonical orbitals shows that the SSCCs are driven by changes in the s-character of the natural atomic orbitals of Pt atoms, which affect the 'Fermi contact' mechanism.
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Affiliation(s)
- Patrick R Batista
- Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
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