1
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Ravindra P, Advincula XR, Schran C, Michaelides A, Kapil V. Quasi-one-dimensional hydrogen bonding in nanoconfined ice. Nat Commun 2024; 15:7301. [PMID: 39181894 PMCID: PMC11344787 DOI: 10.1038/s41467-024-51124-z] [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: 12/31/2023] [Accepted: 07/30/2024] [Indexed: 08/27/2024] Open
Abstract
The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.
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Affiliation(s)
- Pavan Ravindra
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Department of Chemistry, Columbia University, 3000 Broadway, New York, NY, 10027, USA
| | - Xavier R Advincula
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Lennard-Jones Centre, University of Cambridge, Trinity Ln, Cambridge, CB2 1TN, UK
| | - Christoph Schran
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Lennard-Jones Centre, University of Cambridge, Trinity Ln, Cambridge, CB2 1TN, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
- Lennard-Jones Centre, University of Cambridge, Trinity Ln, Cambridge, CB2 1TN, UK.
| | - Venkat Kapil
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
- Lennard-Jones Centre, University of Cambridge, Trinity Ln, Cambridge, CB2 1TN, UK.
- Department of Physics and Astronomy, University College London, 17-19 Gordon St, London, WC1H 0AH, UK.
- Thomas Young Centre and London Centre for Nanotechnology, 19 Gordon St, London, WC1H 0AH, UK.
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2
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Gomez A, Thompson WH, Laage D. Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange. Nat Chem 2024:10.1038/s41557-024-01593-y. [PMID: 39164581 DOI: 10.1038/s41557-024-01593-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/03/2024] [Indexed: 08/22/2024]
Abstract
The transport of excess protons in water is central to acid-base chemistry, biochemistry and energy production. However, elucidating its mechanism has been challenging. Recent nonlinear vibrational spectroscopy experiments could not be explained by existing models. Here we use both vibrational spectroscopy calculations and neural-network-based molecular dynamics simulations that account for nuclear quantum effects for all atoms to determine the proton transport mechanism. Our simulations reveal an equilibrium between two stable proton-localized structures with distinct Eigen-like and Zundel-like hydrogen-bond motifs. Proton transport follows a three-step mechanism gated by two successive hydrogen-bond exchanges: the first reduces the proton-acceptor water coordination, leading to proton transfer, and the second, the rate-limiting step, prevents rapid back-transfer by increasing the proton-donor coordination. This sequential mechanism is consistent with experimental characterizations of proton diffusion, explaining the low activation energy and the prolonged intermediate lifetimes in vibrational spectroscopy. These results are crucial for understanding proton dynamics in biochemical and technological systems.
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Affiliation(s)
- Axel Gomez
- PASTEUR, Department of Chemistry, École normale supérieure, PSL University, Sorbonne Université, CNRS, Paris, France
| | - Ward H Thompson
- Department of Chemistry, University of Kansas, Lawrence, KS, USA
| | - Damien Laage
- PASTEUR, Department of Chemistry, École normale supérieure, PSL University, Sorbonne Université, CNRS, Paris, France.
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3
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Bone RA, Chung MKJ, Ponder JW, Riccardi D, Muzny C, Sundararaman R, Schwarz K. A new method to calculate broadband dielectric spectra of solvents from molecular dynamics simulations demonstrated with polarizable force fields. J Chem Phys 2024; 161:064306. [PMID: 39132799 PMCID: PMC11324330 DOI: 10.1063/5.0217883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 07/26/2024] [Indexed: 08/13/2024] Open
Abstract
Simulating the dielectric spectra of solvents requires the nuanced definition of inter- and intra-molecular forces. Non-polarizable force fields, while thoroughly benchmarked for dielectric applications, do not capture all the spectral features of solvents, such as water. Conversely, polarizable force fields have been largely untested in the context of dielectric spectroscopy but include charge and dipole fluctuations that contribute to intermolecular interactions. We benchmark non-polarizable force fields and the polarizable force fields AMOEBA03 and HIPPO for liquid water and find that the polarizable force fields can capture all the experimentally observed spectral features with varying degrees of accuracy. However, the non-polarizable force fields miss at least one peak. To diagnose this deficiency, we decompose the liquid water spectra from polarizable force fields at multiple temperatures into static and induced dipole contributions and find that the peak originates from induced dipole contributions. Broadening our inquiry to other solvents parameterized with the AMOEBA09 force field, we demonstrate good agreement between the experimental and simulated dielectric spectra of methanol and formamide. To produce these spectra, we develop a new computational approach to calculate the dielectric spectrum via the fluctuation dissipation theorem. This method minimizes the error in both the low and high frequency portions of the spectrum, improving the overall accuracy of the simulated spectrum and broadening the computed frequency range.
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Affiliation(s)
| | - Moses K. J. Chung
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, USA
| | - Jay W. Ponder
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, USA
| | - Demian Riccardi
- Material Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, Colorado 80305, USA
| | - Chris Muzny
- Material Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, Colorado 80305, USA
| | - Ravishankar Sundararaman
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, 110 8th St., Troy, New York 12180, USA
| | - Kathleen Schwarz
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, Maryland 20899, USA
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4
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Zhang H, Juraskova V, Duarte F. Modelling chemical processes in explicit solvents with machine learning potentials. Nat Commun 2024; 15:6114. [PMID: 39030199 PMCID: PMC11271496 DOI: 10.1038/s41467-024-50418-6] [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: 08/06/2023] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.
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Affiliation(s)
- Hanwen Zhang
- Chemistry Research Laboratory, Oxford, United Kingdom
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5
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Roy S, Dürholt JP, Asche TS, Zipoli F, Gómez-Bombarelli R. Learning a reactive potential for silica-water through uncertainty attribution. Nat Commun 2024; 15:6030. [PMID: 39019930 PMCID: PMC11254924 DOI: 10.1038/s41467-024-50407-9] [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: 07/31/2023] [Accepted: 07/03/2024] [Indexed: 07/19/2024] Open
Abstract
The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential trained on PaiNN architecture can accurately capture silicate-water reactivity. The model was trained on a dataset comprising 400,000 energies and forces of molecular clusters at the ωB97X-D3/def2-TZVP level. To ensure the robustness of the model, we introduce a general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.
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Affiliation(s)
- Swagata Roy
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Thomas S Asche
- Evonik Operations GmbH, Essen, North Rhine-Westphalia, Germany
| | - Federico Zipoli
- IBM Research Europe, Saümerstrasse 4, 8803, Rüschlikon, Switzerland
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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6
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O’Neill N, Shi BX, Fong K, Michaelides A, Schran C. To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water. J Phys Chem Lett 2024; 15:6081-6091. [PMID: 38820256 PMCID: PMC11181334 DOI: 10.1021/acs.jpclett.4c01030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/02/2024]
Abstract
The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However, direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller-Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.
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Affiliation(s)
- Niamh O’Neill
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, Cambridge CB3 0HE, United
Kingdom
- Lennard-Jones
Centre, University of Cambridge, Trinity Ln, Cambridge CB2 1TN, United Kingdom
| | - Benjamin X. Shi
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Lennard-Jones
Centre, University of Cambridge, Trinity Ln, Cambridge CB2 1TN, United Kingdom
| | - Kara Fong
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Lennard-Jones
Centre, University of Cambridge, Trinity Ln, Cambridge CB2 1TN, United Kingdom
| | - Angelos Michaelides
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Lennard-Jones
Centre, University of Cambridge, Trinity Ln, Cambridge CB2 1TN, United Kingdom
| | - Christoph Schran
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, Cambridge CB3 0HE, United
Kingdom
- Lennard-Jones
Centre, University of Cambridge, Trinity Ln, Cambridge CB2 1TN, United Kingdom
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7
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Jana A, Shepherd S, Litman Y, Wilkins DM. Learning Electronic Polarizations in Aqueous Systems. J Chem Inf Model 2024; 64:4426-4435. [PMID: 38804973 PMCID: PMC11167596 DOI: 10.1021/acs.jcim.4c00421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/29/2024]
Abstract
The polarization of periodically repeating systems is a discontinuous function of the atomic positions, a fact which seems at first to stymie attempts at their statistical learning. Two approaches to build models for bulk polarizations are compared: one in which a simple point charge model is used to preprocess the raw polarization to give a learning target that is a smooth function of atomic positions and the total polarization is learned as a sum of atom-centered dipoles and one in which instead the average position of Wannier centers around atoms is predicted. For a range of bulk aqueous systems, both of these methods perform perform comparatively well, with the former being slightly better but often requiring an extra effort to find a suitable point charge model. As a challenging test, we also analyze the performance of the models at the air-water interface. In this case, while the Wannier center approach delivers accurate predictions without further modifications, the preprocessing method requires augmentation with information from isolated water molecules to reach similar accuracy. Finally, we present a simple protocol to preprocess the polarizations in a data-driven way using a small number of derivatives calculated at a much lower level of theory, thus overcoming the need to find point charge models without appreciably increasing the computation cost. We believe that the training strategies presented here help the construction of accurate polarization models required for the study of the dielectric properties of realistic complex bulk systems and interfaces with ab initio accuracy.
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Affiliation(s)
- Arnab Jana
- Centre
for Quantum Materials and Technologies, School of Mathematics and
Physics, Queen’s University Belfast, Belfast BT7 1NN, U.K.
| | - Sam Shepherd
- Centre
for Quantum Materials and Technologies, School of Mathematics and
Physics, Queen’s University Belfast, Belfast BT7 1NN, U.K.
| | - Yair Litman
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - David M. Wilkins
- Centre
for Quantum Materials and Technologies, School of Mathematics and
Physics, Queen’s University Belfast, Belfast BT7 1NN, U.K.
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8
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Althorpe SC. Path Integral Simulations of Condensed-Phase Vibrational Spectroscopy. Annu Rev Phys Chem 2024; 75:397-420. [PMID: 38941531 DOI: 10.1146/annurev-physchem-090722-124705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Recent theoretical and algorithmic developments have improved the accuracy with which path integral dynamics methods can include nuclear quantum effects in simulations of condensed-phase vibrational spectra. Such methods are now understood to be approximations to the delocalized classical Matsubara dynamics of smooth Feynman paths, which dominate the dynamics of systems such as liquid water at room temperature. Focusing mainly on simulations of liquid water and hexagonal ice, we explain how the recently developed quasicentroid molecular dynamics (QCMD), fast-QCMD, and temperature-elevated path integral coarse-graining simulations (Te PIGS) methods generate classical dynamics on potentials of mean force obtained by averaging over quantum thermal fluctuations. These new methods give very close agreement with one another, and the Te PIGS method has recently yielded excellent agreement with experimentally measured vibrational spectra for liquid water, ice, and the liquid-air interface. We also discuss the limitations of such methods.
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Affiliation(s)
- Stuart C Althorpe
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom;
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9
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Balicki M, Śmiechowski M. Hydration of N-Hydroxyurea from Ab Initio Molecular Dynamics Simulations. Molecules 2024; 29:2435. [PMID: 38893311 PMCID: PMC11173572 DOI: 10.3390/molecules29112435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024] Open
Abstract
N-Hydroxyurea (HU) is an important chemotherapeutic agent used as a first-line treatment in conditions such as sickle cell disease and β-thalassemia, among others. To date, its properties as a hydrated molecule in the blood plasma or cytoplasm are dramatically understudied, although they may be crucial to the binding of HU to the radical catalytic site of ribonucleotide reductase, its molecular target. The purpose of this work is the comprehensive exploration of HU hydration. The topic is studied using ab initio molecular dynamic (AIMD) simulations that apply a first principles representation of the electron density of the system. This allows for the calculation of infrared spectra, which may be decomposed spatially to better capture the spectral signatures of solute-solvent interactions. The studied molecule is found to be strongly hydrated and tightly bound to the first shell water molecules. The analysis of the distance-dependent spectra of HU shows that the E and Z conformers spectrally affect, on average, 3.4 and 2.5 of the closest H2O molecules, respectively, in spheres of radii of 3.7 Å and 3.5 Å, respectively. The distance-dependent spectra corresponding to these cutoff radii show increased absorbance in the red-shifted part of the water OH stretching vibration band, indicating local enhancement of the solvent's hydrogen bond network. The radially resolved IR spectra also demonstrate that HU effortlessly incorporates into the hydrogen bond network of water and has an enhancing effect on this network. Metadynamics simulations based on AIMD methodology provide a picture of the conformational equilibria of HU in solution. Contrary to previous investigations of an isolated HU molecule in the gas phase, the Z conformer of HU is found here to be more stable by 17.4 kJ·mol-1 than the E conformer, pointing at the crucial role that hydration plays in determining the conformational stability of solutes. The potential energy surface for the OH group rotation in HU indicates that there is no intramolecular hydrogen bond in Z-HU in water, in stark contrast to the isolated solute in the gas phase. Instead, the preferred orientation of the hydroxyl group is perpendicular to the molecular plane of the solute. In view of the known chaotropic effect of urea and its N-alkyl-substituted derivatives, N-hydroxyurea emerges as a unique urea derivative that exhibits a kosmotropic ordering of nearby water. This property may be of crucial importance for its binding to the catalytic site of ribonucleotide reductase with a concomitant displacement of a water molecule.
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Affiliation(s)
| | - Maciej Śmiechowski
- Department of Physical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland;
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10
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Zheng J, Frisch MJ. Multiple-time scale integration method based on an interpolated potential energy surface for ab initio path integral molecular dynamics. J Chem Phys 2024; 160:144111. [PMID: 38597307 DOI: 10.1063/5.0196634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024] Open
Abstract
A new multiple-time scale integration method is presented that propagates ab initio path integral molecular dynamics (PIMD). This method uses a large time step to generate an approximate geometrical configuration whose energy and gradient are evaluated at the level of an ab initio method, and then, a more precise integration scheme, e.g., the Bulirsch-Stoer method or velocity Verlet integration with a smaller time step, is used to integrate from the previous step using the computationally efficient interpolated potential energy surface constructed from two consecutive points. This method makes the integration of PIMD more efficient and accurate compared with the velocity Verlet integration. A Nosé-Hoover chain thermostat combined with this new multiple-time scale method has good energy conservation even with a large time step, which is usually challenging in velocity Verlet integration for PIMD due to the very small chain mass when a large number of beads are used. The new method is used to calculate infrared spectra and free energy profiles to demonstrate its accuracy and capabilities.
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Affiliation(s)
- Jingjing Zheng
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
| | - Michael J Frisch
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
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11
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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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12
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Cassone G, Martelli F. Electrofreezing of liquid water at ambient conditions. Nat Commun 2024; 15:1856. [PMID: 38424051 PMCID: PMC10904787 DOI: 10.1038/s41467-024-46131-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
Water is routinely exposed to external electric fields. Whether, for example, at physiological conditions, in contact with biological systems, or at the interface of polar surfaces in countless technological settings, water responds to fields on the order of a few V Å-1 in a manner that is under intense investigation. Dating back to the 19th century, the possibility of solidifying water upon applying electric fields - a process known as electrofreezing - is an alluring promise that has canalized major efforts since, with uncertain outcomes. Here, we perform long (up to 500 ps per field strength) ab initio molecular dynamics simulations of water at ambient conditions under external electric fields. We show that fields of 0.10 - 0.15 V Å-1 induce electrofreezing to a ferroelectric amorphous phase which we term f-GW (ferroelectric glassy water). The transition occurs after ~ 150 ps for a field of 0.15 V Å-1 and after ~ 200 ps for a field of 0.10 V Å-1 and is signaled by a structural and dynamic arrest and the suppression of the fluctuations of the hydrogen bond network. Our work reports evidence of electrofreezing of bulk liquid water at ambient conditions and therefore impacts several fields, from fundamental chemical physics to biology and catalysis.
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Affiliation(s)
- Giuseppe Cassone
- Institute for Chemical-Physical Processes, National Research Council, Viale F. Stagno d'Alcontres 37, Messina, 98158, Italy.
| | - Fausto Martelli
- IBM Research Europe, Keckwik Lane, Daresbury, WA4 4AD, UK.
- Department of Chemical Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
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13
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Kapil V, Kovács DP, Csányi G, Michaelides A. First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discuss 2024; 249:50-68. [PMID: 37799072 PMCID: PMC10845015 DOI: 10.1039/d3fd00113j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 10/07/2023]
Abstract
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.
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Affiliation(s)
- Venkat Kapil
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | | | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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14
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Dufils T, Schran C, Chen J, Geim AK, Fumagalli L, Michaelides A. Origin of dielectric polarization suppression in confined water from first principles. Chem Sci 2024; 15:516-527. [PMID: 38179530 PMCID: PMC10763014 DOI: 10.1039/d3sc04740g] [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: 09/07/2023] [Accepted: 11/23/2023] [Indexed: 01/06/2024] Open
Abstract
It has long been known that the dielectric constant of confined water should be different from that in bulk. Recent experiments have shown that it is vanishingly small, however the origin of the phenomenon remains unclear. Here we used ab initio molecular dynamics simulations (AIMD) and AIMD-trained machine-learning potentials to understand water's structure and electronic properties underpinning this effect. For the graphene and hexagonal boron-nitride substrates considered, we find that it originates in the spontaneous anti-parallel alignment of the water dipoles in the first two water layers near the solid interface. The interfacial layers exhibit net ferroelectric ordering, resulting in an overall anti-ferroelectric arrangement of confined water. Together with constrained hydrogen-bonding orientations, this leads to much reduced out-of-plane polarization. Furthermore, we directly contrast AIMD and simple classical force-field simulations, revealing important differences. This work offers insight into a property of water that is critical in modulating surface forces, the electric-double-layer formation and molecular solvation, and shows a way to compute it.
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Affiliation(s)
- T Dufils
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - C Schran
- Cavendish Laboratory, Department of Physics, University of Cambridge Cambridge CB3 0HE UK
- Lennard-Jones Centre, University of Cambridge Trinity Ln Cambridge CB2 1TN UK
| | - J Chen
- School of Physics, Peking University Beijing 100871 China
| | - A K Geim
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - L Fumagalli
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - A Michaelides
- Lennard-Jones Centre, University of Cambridge Trinity Ln Cambridge CB2 1TN UK
- Yusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
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15
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LaCour RA, Heindel JP, Head-Gordon T. Predicting the Raman Spectra of Liquid Water with a Monomer-Field Model. J Phys Chem Lett 2023; 14:11742-11749. [PMID: 38116782 DOI: 10.1021/acs.jpclett.3c02873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
The Raman spectrum of liquid water is quite complex, reflecting its strong sensitivity to the local environment of the individual waters. The OH-stretch region of the spectrum, which captures the influence of hydrogen bonding, has only just begun to be unraveled. Here we develop a model for predicting the Raman spectra of the OH-stretch region by considering how local electric fields distort the energy surface of each water monomer. We find that our model is capable of reproducing the bimodal nature of the main peak, with the shoulder at 3250 cm-1 resulting almost entirely from Fermi resonance. Furthermore, we capture the temperature and polarization dependence of the shoulder, which has proven to be difficult to obtain with previous methods, and analyze the origin of this dependence. We expect our model to be generally useful for understanding and predicting how Raman spectra change under different conditions and with different probe reporters beyond water.
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Affiliation(s)
- R Allen LaCour
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, California 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Joseph P Heindel
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, California 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Teresa Head-Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, California 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
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16
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Zhang Y, Wang Y, Xu X, Chen Z, Yang Y. Vibrational Spectra of Highly Anharmonic Water Clusters: Molecular Dynamics and Harmonic Analysis Revisited with Constrained Nuclear-Electronic Orbital Methods. J Chem Theory Comput 2023; 19:9358-9368. [PMID: 38096546 DOI: 10.1021/acs.jctc.3c01037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Vibrational spectroscopy is widely used to gain insights into structural and dynamic properties of chemical, biological, and materials systems. Thus, an efficient and accurate method to simulate vibrational spectra is desired. In this paper, we justify and employ a microcanonical molecular simulation scheme to calculate the vibrational spectra of three challenging water clusters: the neutral water dimer (H4O2), the protonated water trimer (H7O3+), and the protonated water tetramer (H9O4+). We find that with the accurate description of quantum nuclear delocalization effects through the constrained nuclear-electronic orbital framework, including vibrational mode coupling effects through molecular dynamics simulations can additionally improve the vibrational spectrum calculations. In contrast, without the quantum nuclear delocalization picture, conventional ab initio molecular dynamics may even lead to less accurate results than harmonic analysis.
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Affiliation(s)
- Yuzhe Zhang
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Yiwen Wang
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Xi Xu
- Center for Advanced Materials Research, Beijing Normal University, Zhuhai 519087, China
| | - Zehua Chen
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Yang Yang
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
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17
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Wickramasinghe S, Hoehn A, Wetthasinghe ST, Lin H, Wang Q, Jakowski J, Rassolov V, Tang C, Garashchuk S. Theoretical Examination of the Hydroxide Transport in Cobaltocenium-Containing Polyelectrolytes. J Phys Chem B 2023; 127:10129-10141. [PMID: 37972315 DOI: 10.1021/acs.jpcb.3c04118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Polymers incorporating cobaltocenium groups have received attention as promising components of anion-exchange membranes (AEMs), exhibiting a good balance of chemical stability and high ionic conductivity. In this work, we analyze the hydroxide diffusion in the presence of cobaltocenium cations in an aqueous environment based on the molecular dynamics of model systems confined in one dimension to mimic the AEM channels. In order to describe the proton hopping mechanism, the forces are obtained from the electronic structure computed at the density-functional tight-binding level. We find that the hydroxide diffusion depends on the channel size, modulation of the electrostatic interactions by the solvation shell, and its rearrangement ability. Hydroxide diffusion proceeds via both the vehicular and structural diffusion mechanisms with the latter playing a larger role at low diffusion coefficients. The highest diffusion coefficient is observed under moderate water densities (around half the density of liquid water) when there are enough water molecules to form the solvation shell, reducing the electrostatic interaction between ions, yet there is enough space for the water rearrangements during the proton hopping. The effects of cobaltocenium separation, orientation, chemical modifications, and the role of nuclear quantum effects are also discussed.
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Affiliation(s)
- Sachith Wickramasinghe
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Alexandria Hoehn
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Shehani T Wetthasinghe
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Huina Lin
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Qi Wang
- Department of Mathematics, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Jacek Jakowski
- Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Vitaly Rassolov
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Chuanbing Tang
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Sophya Garashchuk
- Department of Chemistry & Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
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18
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Xia J, Zhang Y, Jiang B. Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing. J Phys Chem A 2023; 127:9874-9883. [PMID: 37943102 DOI: 10.1021/acs.jpca.3c06024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.
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Affiliation(s)
- Junfan Xia
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- École Polytechnique FFlytech de Lausanne, 1015 Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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19
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Rashid MAM, Rahman M, Acter T, Uddin N. Identifying the acidic or basic behavior of surface water: a QM/MM-MD study. Phys Chem Chem Phys 2023; 25:31194-31205. [PMID: 37955174 DOI: 10.1039/d3cp02080k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
Controversies on the water surface were theoretically addressed with the help of large scale quantum mechanical molecular dynamics (QMMD) simulations on water surface model systems with and without excess hydroniums and hydroxides. It was revealed that the thermodynamic surface structures of these ions strongly depend on their location and dipole orientation. Fast hydronium diffusion by proton transfer establishes a wider kinetic depth distribution (∼6 Å) than that predicted by its thermodynamic affinity for the water surface, while slow hydroxide is shallowly trapped below the outermost molecular layer (3-4 Å). In addition, the anisotropic orientation of surface water dipole can generate a substantial magnitude of surface potential, which extends to a depth of a few molecular layers. With these distinctively different surface properties of two ions and water molecules, the seemingly contradictory observations of acidic and negatively charged water surfaces may be successfully explained. That is, the negative surface charge of neutral water mostly stems from intrinsic water properties such as water dipole orientation and electron density spillage at the surface, rather than surface OH- ions. The enhanced acidity of the water surface can be attributed in large part to the kinetic depth profile of ion density in addition to static thermodynamic origin. Furthermore, the different depth profiles of the two ions may differently affect the surface-sensitive spectroscopic observations.
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Affiliation(s)
- Md Al Mamunur Rashid
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Mofizur Rahman
- Research and Development Center, Berger Paints Bangladesh Limited, Berger House, Dhaka-1230, Bangladesh
| | - Thamina Acter
- Department of Mathematical and Physical Sciences, East West University, Aftabnagar, Dhaka-1212, Bangladesh
| | - Nizam Uddin
- Department of Nutrition and Food Engineering, Daffodil International University, Birulia, Dhaka-1216, Bangladesh.
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20
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Shen H, Shen X, Wu Z. Simulating the isotropic Raman spectra of O-H stretching mode in liquid H 2O based on a machine learning potential: the influence of vibrational couplings. Phys Chem Chem Phys 2023; 25:28180-28188. [PMID: 37819214 DOI: 10.1039/d3cp03035k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
In this study, we trained a deep potential (DP) for H2O, an accurate machine learning (ML) potential. We performed molecular dynamics (MD) simulations of liquid water using the DP model (or DeePMD simulations). Our results showed that the DP model exhibits DFT-level accuracy, and the DeePMD simulation is a promising approach for modeling the structural properties of liquid water. Based on the DeePMD simulation trajectories, we calculated the isotropic Raman spectra of the O-H stretching mode using the surface-specific velocity-velocity correlation function (ssVVCF), showing that the DeePMD/ssVVCF approach can correctly capture the bimodal characteristics of the experimental Raman spectra, with one peak located near 3400 cm-1 and the other near 3250 cm-1. The success of the DeePMD/ssVVCF approach should be credited to (1) the DFT-level accuracy of the DP model for H2O, (2) the ssVVCF formulation considering the coupling between vibrational modes, and (3) non-Condon effects. Furthermore, the DeePMD simulations revealed that the anharmonic interactions between the coupled water molecules in the first and second hydration shells should play an essential role in the strong mixing of the H-O-H bending mode and the O-H stretching mode, leading to the delocalization of the O-H stretching band. In particular, increasing the strength of hydrogen bonds would enhance the bend-stretch coupling, leading to the red-shifting of the O-H vibrational spectra and the increase in the intensity of the shoulder around 3250 cm-1.
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Affiliation(s)
- Hujun Shen
- Guizhou Provincial Key Laboratory of Computational Nano-Material Science, Guizhou Education University, Guiyang 550018, China.
| | - Xu Shen
- National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhenhua Wu
- Department of Big Data and Artificial Intelligence, Guizhou Vocational Technology College of Electronics & Information, Kaili, 556000, China
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21
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Feldman YMY, Hirshberg B. Quadratic scaling bosonic path integral molecular dynamics. J Chem Phys 2023; 159:154107. [PMID: 37855315 DOI: 10.1063/5.0173749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023] Open
Abstract
Bosonic exchange symmetry leads to fascinating quantum phenomena, from exciton condensation in quantum materials to the superfluidity of liquid 4He. Unfortunately, path integral molecular dynamics (PIMD) simulations of bosons are computationally prohibitive beyond ∼100 particles, due to a cubic scaling with the system size. We present an algorithm that reduces the complexity from cubic to quadratic, allowing the first simulations of thousands of bosons using PIMD. Our method is orders of magnitude faster, with a speedup that scales linearly with the number of particles and the number of imaginary time slices (beads). Simulations that would have otherwise taken decades can now be done in days. In practice, the new algorithm eliminates most of the added computational cost of including bosonic exchange effects, making them almost as accessible as PIMD simulations of distinguishable particles.
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Affiliation(s)
- Yotam M Y Feldman
- School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
- The Ratner Center for Single Molecule Science, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Barak Hirshberg
- School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
- The Ratner Center for Single Molecule Science, Tel Aviv University, Tel Aviv 6997801, Israel
- The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 6997801, Israel
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22
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Liu L, Tian Y, Yang X, Liu C. Mechanistic Insights into Water Autoionization through Metadynamics Simulation Enhanced by Machine Learning. PHYSICAL REVIEW LETTERS 2023; 131:158001. [PMID: 37897750 DOI: 10.1103/physrevlett.131.158001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 07/05/2023] [Accepted: 08/21/2023] [Indexed: 10/30/2023]
Abstract
Characterizing the free energy landscape of water ionization has been a great challenge due to the limitations from expensive ab initio calculations and strong rare-event features. Lacking equilibrium sampling of the ionization pathway will cause ambiguities in the mechanistic study. Here, we obtain convergent free energy surfaces through nanosecond timescale metadynamics simulations with classical nuclei enhanced by atomic neural network potentials, which yields good reproduction of the equilibrium constant (pK_{w}=14.14) and ionization rate constant (1.369×10^{-3} s^{-1}). The character of transition state unveils the triple-proton transfer occurs through a concerted but asynchronous mechanism. Conditional ensemble average analyses establish the dual-presolvation mechanism, where a pair of hypercoordinated and undercoordinated waters bridged by one H_{2}O cooperatively constitutes the initiation environment for autoionization, and contributes extremely to the local electric field fluctuation to promote water dissociation.
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Affiliation(s)
- Ling Liu
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yingqi Tian
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Xuanye Yang
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Chungen Liu
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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23
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Zhang Y, Jiang B. Universal machine learning for the response of atomistic systems to external fields. Nat Commun 2023; 14:6424. [PMID: 37827998 PMCID: PMC10570356 DOI: 10.1038/s41467-023-42148-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/01/2023] [Indexed: 10/14/2023] Open
Abstract
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.
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Affiliation(s)
- Yaolong Zhang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China
- École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, 230088, China.
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24
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Khuu T, Schleif T, Mohamed A, Mitra S, Johnson MA, Valdiviezo J, Heindel JP, Head-Gordon T. Intra-cluster Charge Migration upon Hydration of Protonated Formic Acid Revealed by Anharmonic Analysis of Cold Ion Vibrational Spectra. J Phys Chem A 2023; 127:7501-7509. [PMID: 37669457 DOI: 10.1021/acs.jpca.3c03971] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
The rates of many chemical reactions are accelerated when carried out in micron-sized droplets, but the molecular origin of the rate acceleration remains unclear. One example is the condensation reaction of 1,2-diaminobenzene with formic acid to yield benzimidazole. The observed rate enhancements have been rationalized by invoking enhanced acidity at the surface of methanol solvent droplets with low water content to enable protonation of formic acid to generate a cationic species (protonated formic acid or PFA) formed by attachment of a proton to the neutral acid. Because PFA is the key feature in this reaction mechanism, vibrational spectra of cryogenically cooled, microhydrated PFA·(H2O)n=1-6 were acquired to determine how the extent of charge localization depends on the degree of hydration. Analysis of these highly anharmonic spectra with path integral ab initio molecular dynamics simulations reveals the gradual displacement of the excess proton onto the water network in the microhydration regime at low temperatures with n = 3 as the tipping point for intra-cluster proton transfer.
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Affiliation(s)
- Thien Khuu
- Sterling Chemistry Laboratory, Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Tim Schleif
- Sterling Chemistry Laboratory, Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Ahmed Mohamed
- Sterling Chemistry Laboratory, Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Sayoni Mitra
- Sterling Chemistry Laboratory, Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Mark A Johnson
- Sterling Chemistry Laboratory, Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Jesús Valdiviezo
- Pitzer Theory Center, Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Joseph P Heindel
- Pitzer Theory Center, Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Teresa Head-Gordon
- Pitzer Theory Center, Department of Chemistry, University of California, Berkeley, California 94720, United States
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25
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Yu Q, Qu C, Houston PL, Nandi A, Pandey P, Conte R, Bowman JM. A Status Report on "Gold Standard" Machine-Learned Potentials for Water. J Phys Chem Lett 2023; 14:8077-8087. [PMID: 37656898 PMCID: PMC10510435 DOI: 10.1021/acs.jpclett.3c01791] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/28/2023] [Indexed: 09/03/2023]
Abstract
Owing to the central importance of water to life as well as its unusual properties, potentials for water have been the subject of extensive research over the past 50 years. Recently, five potentials based on different machine learning approaches have been reported that are at or near the "gold standard" CCSD(T) level of theory. The development of such high-level potentials enables efficient and accurate simulations of water systems using classical and quantum dynamical approaches. This Perspective serves as a status report of these potentials, focusing on their methodology and applications to water systems across different phases. Their performances on the energies of gas phase water clusters, as well as condensed phase structural and dynamical properties, are discussed.
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Affiliation(s)
- Qi Yu
- Department
of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Chen Qu
- Independent
Researcher, Toronto, Ontario M9B 0E3, Canada
| | - Paul L. Houston
- Department
of Chemistry and Chemical Biology, Cornell
University, Ithaca, New York 14853, United States
- Department of Chemistry
and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Apurba Nandi
- Department
of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Priyanka Pandey
- Department
of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Riccardo Conte
- Dipartimento
di Chimica, Università degli Studi
di Milano, via Golgi 19, 20133 Milano, Italy
| | - Joel M. Bowman
- Department
of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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26
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Litman Y, Lan J, Nagata Y, Wilkins DM. Fully First-Principles Surface Spectroscopy with Machine Learning. J Phys Chem Lett 2023; 14:8175-8182. [PMID: 37671886 PMCID: PMC10510433 DOI: 10.1021/acs.jpclett.3c01989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023]
Abstract
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.
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Affiliation(s)
- Yair Litman
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Max
Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Jinggang Lan
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| | - Yuki Nagata
- Max
Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - David M. Wilkins
- Centre
for Quantum Materials and Technologies School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
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27
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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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28
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Chen BWJ, Zhang X, Zhang J. Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials. Chem Sci 2023; 14:8338-8354. [PMID: 37564405 PMCID: PMC10411631 DOI: 10.1039/d3sc02482b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/11/2023] [Indexed: 08/12/2023] Open
Abstract
Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.
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Affiliation(s)
- Benjamin W J Chen
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Xinglong Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Jia Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
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29
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Kovács DP, Batatia I, Arany ES, Csányi G. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science. J Chem Phys 2023; 159:044118. [PMID: 37522405 DOI: 10.1063/5.0155322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.
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Affiliation(s)
- Dávid Péter Kovács
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Ilyes Batatia
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Eszter Sára Arany
- School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, United Kingdom
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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30
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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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31
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Roterman I, Konieczny L. Protein Is an Intelligent Micelle. ENTROPY (BASEL, SWITZERLAND) 2023; 25:850. [PMID: 37372194 DOI: 10.3390/e25060850] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/22/2023] [Accepted: 04/28/2023] [Indexed: 06/29/2023]
Abstract
Interpreting biological phenomena at the molecular and cellular levels reveals the ways in which information that is specific to living organisms is processed: from the genetic record contained in a strand of DNA, to the translation process, and then to the construction of proteins that carry the flow and processing of information as well as reveal evolutionary mechanisms. The processing of a surprisingly small amount of information, i.e., in the range of 1 GB, contains the record of human DNA that is used in the construction of the highly complex system that is the human body. This shows that what is important is not the quantity of information but rather its skillful use-in other words, this facilitates proper processing. This paper describes the quantitative relations that characterize information during the successive steps of the "biological dogma", illustrating a transition from the recording of information in a DNA strand to the production of proteins exhibiting a defined specificity. It is this that is encoded in the form of information and that determines the unique activity, i.e., the measure of a protein's "intelligence". In a situation of information deficit at the transformation stage of a primary protein structure to a tertiary or quaternary structure, a particular role is served by the environment as a supplier of complementary information, thus leading to the achievement of a structure that guarantees the fulfillment of a specified function. Its quantitative evaluation is possible via using a "fuzzy oil drop" (FOD), particularly with respect to its modified version. This can be achieved when taking into account the participation of an environment other than water in the construction of a specific 3D structure (FOD-M). The next step of information processing on the higher organizational level is the construction of the proteome, where the interrelationship between different functional tasks and organism requirements can be generally characterized by homeostasis. An open system that maintains the stability of all components can be achieved exclusively in a condition of automatic control that is realized by negative feedback loops. This suggests a hypothesis of proteome construction that is based on the system of negative feedback loops. The purpose of this paper is the analysis of information flow in organisms with a particular emphasis on the role of proteins in this process. This paper also presents a model introducing the component of changed conditions and its influence on the protein folding process-since the specificity of proteins is coded in their structure.
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Affiliation(s)
- Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Medyczna 7, 30-688 Kraków, Poland
| | - Leszek Konieczny
- Chair of Medical Biochemistry, Jagiellonian University-Medical College, Kopernika 7, 31-034 Kraków, Poland
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Ferretti A, Canal L, Sorodoc RA, Sinha S, Brancato G. Fine Tuning the Intermolecular Interactions of Water Clusters Using the Dispersion-Corrected Density Functional Theory. Molecules 2023; 28:molecules28093834. [PMID: 37175249 PMCID: PMC10180381 DOI: 10.3390/molecules28093834] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Dispersion-inclusive density functional theory (DFT) methods have unequivocally demonstrated improved performances with respect to standard DFT approximations for modeling large and extended molecular systems at the quantum mechanical level. Yet, in some cases, disagreements with highly accurate reference calculations, such as CCSD(T) and quantum Monte Carlo (MC) calculations, still remain. Furthermore, the application of general-purpose corrections, such as the popular Grimme's semi-classical models (DFT-D), to different Kohn-Sham exchange-correlation functionals sometimes leads to variable and inconsistent results, which recommend a careful prior evaluation. In a recent study, we proposed a simple optimization protocol for enhancing the accuracy of these DFT-D methods by following an alternative and system-specific approach. Here, adopting the same computational strategy, we show how the accurate MC intermolecular interactions of a large set of water clusters of variable sizes (i.e., 300 (H2O)n structures, n = 9, 15, 27) can be reproduced remarkably well by dispersion-corrected DFT models (i.e., B3LYP-D4, PBE-D4, revPBE(0)-D4) upon re-optimization, reaching a mean absolute error per monomer of ~0.1 kcal/mol. Hence, the obtained results support the use of this procedure for fine-tuning tailored DFT-D models for the accurate description of targeted molecular systems.
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Affiliation(s)
- Alfonso Ferretti
- Scuola Normale Superiore and CSGI, Classe di Scienze, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
- Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
| | - Laura Canal
- Dipartimento di Ingegneria Civile ed Industriale, Università di Pisa, Largo Lucio Lazzarino 2, I-56124 Pisa, Italy
| | - Robert A Sorodoc
- Dipartimento di Ingegneria Civile ed Industriale, Università di Pisa, Largo Lucio Lazzarino 2, I-56124 Pisa, Italy
| | - Sourab Sinha
- Scuola Normale Superiore and CSGI, Classe di Scienze, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
| | - Giuseppe Brancato
- Scuola Normale Superiore and CSGI, Classe di Scienze, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
- Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
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33
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Begušić T, Blake GA. Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality. Nat Commun 2023; 14:1950. [PMID: 37029146 PMCID: PMC10082090 DOI: 10.1038/s41467-023-37667-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/22/2023] [Indexed: 04/09/2023] Open
Abstract
Two-dimensional spectroscopic techniques combining terahertz (THz), infrared (IR), and visible pulses offer a wealth of information about coupling among vibrational modes in molecular liquids, thus providing a promising probe of their local structure. However, the capabilities of these spectroscopies are still largely unexplored due to experimental limitations and inherently weak nonlinear signals. Here, through a combination of equilibrium-nonequilibrium molecular dynamics (MD) and a tailored spectrum decomposition scheme, we identify a relationship between the tetrahedral order of liquid water and its two-dimensional IR-IR-Raman (IIR) spectrum. The structure-spectrum relationship can explain the temperature dependence of the spectral features corresponding to the anharmonic coupling between low-frequency intermolecular and high-frequency intramolecular vibrational modes of water. In light of these results, we propose new experiments and discuss the implications for the study of tetrahedrality of liquid water.
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Affiliation(s)
- Tomislav Begušić
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
| | - Geoffrey A Blake
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, 91125, USA.
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34
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Inoue K, Litman Y, Wilkins DM, Nagata Y, Okuno M. Is Unified Understanding of Vibrational Coupling of Water Possible? Hyper-Raman Measurement and Machine Learning Spectra. J Phys Chem Lett 2023; 14:3063-3068. [PMID: 36947156 DOI: 10.1021/acs.jpclett.3c00398] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The impact of the vibrational coupling of the OH stretch mode on the spectra differs significantly between IR and Raman spectra of water. Unified understanding of the vibrational couplings is not yet achieved. By using a different class of vibrational spectroscopy, hyper-Raman (HR) spectroscopy, together with machine-learning-assisted HR spectra calculation, we examine the impact of the vibrational couplings of water through the comparison of isotopically diluted H2O and pure H2O. We found that the isotopic dilution reduces the HR bandwidths, but the impact of the vibrational coupling is smaller than in the IR and parallel-polarized Raman. Machine learning HR spectra indicate that the intermolecular coupling plays a major role in broadening the bandwidth, while the intramolecular coupling is negligibly small, which is consistent with the IR and Raman spectra. Our result clearly demonstrates a limited impact of the intramolecular vibration, independent of the selection rules of vibrational spectroscopies.
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Affiliation(s)
- Kazuki Inoue
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan
| | - Yair Litman
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - David M Wilkins
- Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| | - Yuki Nagata
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Masanari Okuno
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan
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35
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Gao A, Remsing RC, Weeks JD. Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions. J Phys Chem B 2023; 127:809-821. [PMID: 36669139 DOI: 10.1021/acs.jpcb.2c06988] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions but present conceptual and computational challenges to both theory and simulations. We review recent developments in an approach addressing these challenges─local molecular field (LMF) theory. LMF theory exploits an exact and physically suggestive separation of intermolecular Coulomb interactions into strong short-range and uniformly slowly varying long-range components. This allows us to accurately determine the averaged effects of the long-range components on the short-range structure using effective single particle fields and analytical corrections, greatly reducing the need for complex lattice summation techniques used in most standard approaches. The simplest use of these ideas in aqueous solutions leads to the short solvent (SS) model, where both solvent-solvent and solute-solvent Coulomb interactions have only short-range components. Here we use the SS model to give a simple description of pairing of nucleobases and biologically relevant ions in water.
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Affiliation(s)
- Ang Gao
- Department of Physics, Beijing University of Posts and Telecommunications, Beijing, China 100876
| | - Richard C Remsing
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - John D Weeks
- Institute for Physical Science and Technology and Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, United States
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36
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Ikeda T. First principles molecular dynamics study of proton disorder in C1′ phase of H2 hydrate. Chem Phys Lett 2023. [DOI: 10.1016/j.cplett.2022.140252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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37
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Liu Y, Li C, Voth GA. Generalized Transition State Theory Treatment of Water-Assisted Proton Transport Processes in Proteins. J Phys Chem B 2022; 126:10452-10459. [PMID: 36459423 PMCID: PMC9762399 DOI: 10.1021/acs.jpcb.2c06703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/15/2022] [Indexed: 12/03/2022]
Abstract
Transition state theory (TST) is widely employed for estimating the transition rate of a reaction when combined with free energy sampling techniques. A derivation of the transition theory rate expression for a general n-dimensional case is presented in this work which specifically focuses on water-assisted proton transfer/transport reactions, especially for protein systems. Our work evaluates the TST prefactor calculated at the transition state dividing surface compared to one sampled, as an approximation, in the reactant state in four case studies of water-assisted proton transport inside membrane proteins and highlights the significant impact of the prefactor position dependence in proton transport processes.
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Affiliation(s)
- Yu Liu
- Department of Chemistry, Chicago Center
for Theoretical Chemistry, James Franck Institute, and Institute for
Biophysical Dynamics, The University of
Chicago, Chicago, Illinois60637, United States
| | - Chenghan Li
- Department of Chemistry, Chicago Center
for Theoretical Chemistry, James Franck Institute, and Institute for
Biophysical Dynamics, The University of
Chicago, Chicago, Illinois60637, United States
| | - Gregory A. Voth
- Department of Chemistry, Chicago Center
for Theoretical Chemistry, James Franck Institute, and Institute for
Biophysical Dynamics, The University of
Chicago, Chicago, Illinois60637, United States
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38
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Ganeshan K, Khanal R, Muraleedharan MG, Hellström M, Kent PRC, Irle S, van Duin ACT. Importance of Nuclear Quantum Effects on Aqueous Electrolyte Transport under Confinement in Ti 3C 2 MXenes. J Chem Theory Comput 2022; 18:6920-6931. [PMID: 36269878 DOI: 10.1021/acs.jctc.2c00771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protons display a high chemical activity and strongly affect the charge storage capability in confined interlayer spaces of two-dimensional (2D) materials. As such, an accurate representation of proton dynamics under confinement is important for understanding and predicting charge storage dynamics in these materials. While often ignored in atomistic-scale simulations, nuclear quantum effects (NQEs), e.g., tunneling, can be significant under confinement even at room temperature. Using the thermostatted ring polymer molecular dynamics implementation of path integral molecular dynamics (PIMD) in conjunction with the ReaxFF force field, density functional tight binding (DFTB), and NequIP neural network potential simulations, we investigate the role of NQEs on proton and water transport in bulk water and aqueous electrolytes under confinement in Ti3C2 MXenes. Although overall NQEs are relatively small, especially in bulk, we find that they can alter both quantitative values and qualitative trends on both proton transport and water self-diffusion under confinement relative to classical MD predictions. Therefore, our results suggest the need for NQEs to be considered to simulate aqueous systems under confinement for both qualitative and quantitative accuracy.
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Affiliation(s)
- Karthik Ganeshan
- Department of Mechanical Engineering, Pennsylvania State University, University Park, Pennsylvania16802, United States
| | - Rabi Khanal
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United States
| | - Murali Gopal Muraleedharan
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United States
| | - Matti Hellström
- Software for Chemistry and Materials B.V., Amsterdam1081HV, The Netherlands
| | - Paul R C Kent
- Center for Nanophase Materials Sciences and Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United States
| | - Stephan Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United States
| | - Adri C T van Duin
- Department of Mechanical Engineering, Pennsylvania State University, University Park, Pennsylvania16802, United States
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39
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Hao H, Ruiz Pestana L, Qian J, Liu M, Xu Q, Head‐Gordon T. Chemical transformations and transport phenomena at interfaces. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Hongxia Hao
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Luis Ruiz Pestana
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Jin Qian
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Meili Liu
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Qiang Xu
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Teresa Head‐Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
- Department of Bioengineering and Chemical and Biomolecular Engineering University of California Berkeley California USA
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40
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The first-principles phase diagram of monolayer nanoconfined water. Nature 2022; 609:512-516. [PMID: 36104556 DOI: 10.1038/s41586-022-05036-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 06/28/2022] [Indexed: 11/09/2022]
Abstract
Water in nanoscale cavities is ubiquitous and of central importance to everyday phenomena in geology and biology. However, the properties of nanoscale water can be substantially different from those of bulk water, as shown, for example, by the anomalously low dielectric constant of water in nanochannels1, near frictionless water flow2 or the possible existence of a square ice phase3. Such properties suggest that nanoconfined water could be engineered for technological applications in nanofluidics4, electrolyte materials5 and water desalination6. Unfortunately, challenges in experimentally characterizing water at the nanoscale and the high cost of first-principles simulations have prevented the molecular-level understanding required to control the behaviour of water. Here we combine a range of computational approaches to enable a first-principles-level investigation of a single layer of water within a graphene-like channel. We find that monolayer water exhibits surprisingly rich and diverse phase behaviour that is highly sensitive to temperature and the van der Waals pressure acting within the nanochannel. In addition to multiple molecular phases with melting temperatures varying non-monotonically by more than 400 kelvins with pressure, we predict a hexatic phase, which is an intermediate between a solid and a liquid, and a superionic phase with a high electrical conductivity exceeding that of battery materials. Notably, this suggests that nanoconfinement could be a promising route towards superionic behaviour under easily accessible conditions.
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41
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Jung KA, Markland TE. 2D spectroscopies from condensed phase dynamics: Accessing third-order response properties from equilibrium multi-time correlation functions. J Chem Phys 2022; 157:094111. [DOI: 10.1063/5.0107087] [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
The third-order response lies at the heart of simulating and interpreting nonlinear spectroscopies ranging from two dimensional infrared (2D-IR) to 2D electronic (2D-ES), and 2D sum frequency generation (2D-SFG). The extra time and frequency dimensions in these spectroscopies provides access to rich information on the electronic and vibrational states present, the coupling between them, and the resulting rates at which they exchange energy that are obscured in linear spectroscopy, particularly for condensed phase systems that usually contain many overlapping features. While the exact quantum expression for the third-order response is well established it is incompatible with the methods that are practical for calculating the atomistic dynamics of large condensed phase systems. These methods, which include both classical mechanics and quantum dynamics methods that retain quantum statistical properties while obeying the symmetries of classical dynamics, such as LSC-IVR, Centroid Molecular Dynamics (CMD) and Ring Polymer Molecular Dynamics (RPMD) naturally provide short-time approximations to the multi-time symmetrized Kubo transformed correlation function. Here, we show how the third-order response can be formulated in terms of equilibrium symmetrized Kubo transformed correlation functions. We demonstrate the utility and accuracy of our approach by showing how it can be used to obtain the third-order response of a series of model systems using both classical dynamics and RPMD. In particular, we show that this approach captures features such as anharmonically induced vertical splittings and peak shifts while providing a physically transparent framework for understanding multidimensional spectroscopies.
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42
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Palchowdhury S, Mukherjee K, Maroncelli M. Rapid Water Dynamics Structures the OH-Stretching Spectra of Solitary Water in Ionic Liquids and Dipolar Solvents. J Chem Phys 2022; 157:084502. [DOI: 10.1063/5.0107348] [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
In a recent study [ J. Phys. Chem. B 126, 4584 (2022)] we used infrared spectroscopy to investigate the solvation and dynamics of solitary water in ionic liquids and dipolar solvents. Complex shapes observed for water OH-stretching bands common to all high-polarity solvents were assigned to water in several solvation states. In the present study, classical molecular dynamics simulations of a single water molecule in four ionic liquids and three dipolar solvents were used to test and refine this interpretation. Consistent with past assignments, simulations show solitary water usually donates two hydrogen bonds to distinct solvent molecules. Such symmetrically solvated water produces the primary pair of peaks identified in the OH spectra of water in nearly all solvents. We had further proposed that additional features flanking this main peak are due to asymmetric solvation states, states in which only one OH group makes a hydrogen bond to solvent. Such states were found in significant concentrations in all of the systems simulated. Simulations of the OH stretching spectra using a semiclassical description and the vibrational map developed by Auer and Skinner [ J. Chem. Phys. 128, 224511 (2008)] provided semi-quantitative agreement with experiment. Analysis of species-specific spectra also confirmed assignment of the additional features in the experimental spectra to asymmetrically solvated water. The simulations also showed that rapid water motions cause a marked motional narrowing compared to the inhomogeneous limit, and that this narrowing is largely responsible for making the additional features due to minority solvation states manifest in the spectra.
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Affiliation(s)
- Souarv Palchowdhury
- The Pennsylvania State University - University Park Campus, United States of America
| | - Kallol Mukherjee
- The Pennsylvania State University - University Park Campus, United States of America
| | - Mark Maroncelli
- Department of Chemsitry, The Pennsylvania State University - University Park Campus, United States of America
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43
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Thiemann F, Schran C, Rowe P, Müller EA, Michaelides A. Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick. ACS NANO 2022; 16:10775-10782. [PMID: 35726839 PMCID: PMC9331139 DOI: 10.1021/acsnano.2c02784] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Experimental measurements have reported ultrafast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behavior is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials, with water experiencing indeed a ≈5 times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of quantum mechanical accuracy for water flow in single-wall nanotubes but also provides detailed mechanistic insight into its radius and material dependence for future technological application.
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Affiliation(s)
- Fabian
L. Thiemann
- Thomas
Young Centre, London Centre for Nanotechnology and Department of Physics
and Astronomy, University College London, Gower Street, London WC1E 6BT, United
Kingdom
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Department
of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Christoph Schran
- Thomas
Young Centre, London Centre for Nanotechnology and Department of Physics
and Astronomy, University College London, Gower Street, London WC1E 6BT, United
Kingdom
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Patrick Rowe
- Thomas
Young Centre, London Centre for Nanotechnology and Department of Physics
and Astronomy, University College London, Gower Street, London WC1E 6BT, United
Kingdom
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Erich A. Müller
- Department
of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Angelos Michaelides
- Thomas
Young Centre, London Centre for Nanotechnology and Department of Physics
and Astronomy, University College London, Gower Street, London WC1E 6BT, United
Kingdom
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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44
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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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45
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Joutsuka T. Molecular Mechanism of Autodissociation in Liquid Water: Ab Initio Molecular Dynamics Simulations. J Phys Chem B 2022; 126:4565-4571. [PMID: 35694850 DOI: 10.1021/acs.jpcb.2c01971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Autodissociation in liquid water is one of the most important processes in various topics of physical chemistry, such as acid-base chemistry. Molecular simulations have elucidated most of the molecular mechanisms at the atomic level, yet quantitative analysis to compare with experiments using the potential of mean force (PMF) remains a hurdle, including the definition of reaction coordinates and the accuracy of liquid structures by ab initio molecular dynamics (AIMD) simulations with density functional theory (DFT) methods. Here, we perform AIMD simulations with the revPBE-D3 exchange-correlation functional to compute the PMF profiles of autoionization, or proton transfer (PT), in liquid water. For the quantitative analysis with physically meaningful reaction coordinates, we employ a PT coordinate, donor-acceptor (OH--H3O+) distance, and hydrogen (H)-bond number. The one-dimensional (1D) PMF profile along the PT coordinate shows no local minimum in the product state of PT (OH- and H3O+), which is necessary to accurately compute the acid dissociation constant (or pKa). On the other hand, the 2D PMF profiles along the PT coordinate and donor-acceptor distance show local minima in the product state and reaction barriers, and the computed pKw is comparable to the experiment. In addition, the 2D PMF profiles along the PT coordinate and the H-bond number reveal the molecular mechanism of the H-bond rearrangement concomitant with PT, in which the H-bond breaking before PT is slightly preferable. These findings indicate that an accurate evaluation of pKa by MD simulations requires the donor-acceptor distance in addition to the conventional PT coordinate.
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Affiliation(s)
- Tatsuya Joutsuka
- Institute of Quantum Beam Science, Graduate School of Science and Engineering, Ibaraki University, Hitachi 316-8511 Ibaraki, Japan.,Frontier Research Center for Applied Atomic Sciences, Ibaraki University, 162-1 Shirakata, Tokai, 319-1106 Ibaraki, Japan
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46
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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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47
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Herrero C, Pauletti M, Tocci G, Iannuzzi M, Joly L. Connection between water's dynamical and structural properties: Insights from ab initio simulations. Proc Natl Acad Sci U S A 2022; 119:e2121641119. [PMID: 35588447 PMCID: PMC9173753 DOI: 10.1073/pnas.2121641119] [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: 12/13/2021] [Accepted: 04/12/2022] [Indexed: 01/25/2023] Open
Abstract
SignificanceFirst-principles calculations, which explicitly account for the electronic structure of matter, can shed light on the molecular structure and dynamics of water in its supercooled state. In this work, we use density functional theory, which relies on a functional to describe electronic exchange and correlations, to evaluate which functional best describes the temperature evolution of bulk water transport coefficients. We also assess the validity of the Stokes-Einstein relation for all the functionals in the temperature range studied, and explore the link between structure and dynamics. Based on these results, we show how transport coefficients can be computed from structural descriptors, which require shorter simulation times to converge, and we point toward strategies to develop better functionals.
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Affiliation(s)
- Cecilia Herrero
- Univ Lyon, Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France
| | - Michela Pauletti
- Department of Chemistry, Universität Zürich, 8057 Zürich, Switzerland
| | - Gabriele Tocci
- Department of Chemistry, Universität Zürich, 8057 Zürich, Switzerland
| | - Marcella Iannuzzi
- Department of Chemistry, Universität Zürich, 8057 Zürich, Switzerland
| | - Laurent Joly
- Univ Lyon, Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France
- Institut Universitaire de France (IUF), 75005 Paris, France
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48
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Thomsen B, Shiga M. Structures of liquid and aqueous water isotopologues at ambient temperature from ab initio path integral simulations. Phys Chem Chem Phys 2022; 24:10851-10859. [PMID: 35504275 DOI: 10.1039/d2cp00499b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The heavy hydrogen isotopes D and T are found in trace amounts in water, and when their concentration increases they can play an intricate role in modulating the physical properties of the liquid. We present an analysis of the microscopic structures of ambient light water (H2O(l)), heavy water (D2O(l)), T2O(l), HDO(aq) and HTO(aq) studied by ab initio path integral molecular dynamics (PIMD). Unlike previous ab initio PIMD investigations of H2O(l) and D2O(l) [Chen et al., Phys. Rev. Lett., 2003, 91, 215503] [Machida et al., J. Chem. Phys., 2017, 148, 102324] we find that D2O(l) is more structured than H2O(l), as is predicted by the experiment. The agreement between the experiment and our simulation for H2O(l) and D2O(l) allows us to accurately predict the intra- and intermolecular structures of T2O(l) HDO(aq) and HTO(aq). T2O(l) is found to have a similar intermolecular structure to that of D2O(l), while the intramolecular structure is more compact, giving rise to a smaller dipole moment than those of H2O(l) and D2O(l). For the mixed isotope species, HDO(aq) and HTO(aq), we find smaller dipole moments and fewer hydrogen bonds when compared with the pure species H2O and D2O. We can attribute this effect to the relative compactness of the mixed isotope species, which results in a lower dipole moment than that of the pure species.
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Affiliation(s)
- Bo Thomsen
- CCSE, Japan Atomic Energy Agency, 178-4-4, Wakashiba, Kashiwa, Chiba, 277-0871, Japan.
| | - Motoyuki Shiga
- CCSE, Japan Atomic Energy Agency, 178-4-4, Wakashiba, Kashiwa, Chiba, 277-0871, Japan.
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49
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Li C, Paesani F, Voth GA. Static and Dynamic Correlations in Water: Comparison of Classical Ab Initio Molecular Dynamics at Elevated Temperature with Path Integral Simulations at Ambient Temperature. J Chem Theory Comput 2022; 18:2124-2131. [PMID: 35263110 PMCID: PMC9059465 DOI: 10.1021/acs.jctc.1c01223] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
It is a common practice in ab initio molecular dynamics (AIMD) simulations of water to use an elevated temperature to overcome the overstructuring and slow diffusion predicted by most current density functional theory (DFT) models. The simulation results obtained in this distinct thermodynamic state are then compared with experimental data at ambient temperature based on the rationale that a higher temperature effectively recovers nuclear quantum effects (NQEs) that are missing in the classical AIMD simulations. In this work, we systematically examine the foundation of this assumption for several DFT models as well as for the many-body MB-pol model. We find for the cases studied that a higher temperature does not correctly mimic NQEs at room temperature, which is especially manifest in significantly different three-molecule correlations as well as hydrogen bond dynamics. In many of these cases, the effects of NQEs are the opposite of the effects of carrying out the simulations at an elevated temperature.
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Affiliation(s)
- Chenghan Li
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, Materials Science and Engineering, and San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093
| | - Gregory A. Voth
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637
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50
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Gao A, Remsing RC. 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] [Key Words] [MESH Headings] [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.
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Affiliation(s)
- Ang Gao
- Department of Physics, Beijing University of Posts and Telecommunications, 100876, Beijing, China.
| | - Richard C Remsing
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, 08854, USA.
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