1
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Litman Y, Chiang KY, Seki T, Nagata Y, Bonn M. Surface stratification determines the interfacial water structure of simple electrolyte solutions. Nat Chem 2024; 16:644-650. [PMID: 38225269 PMCID: PMC10997511 DOI: 10.1038/s41557-023-01416-6] [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: 09/20/2022] [Accepted: 12/07/2023] [Indexed: 01/17/2024]
Abstract
The distribution of ions at the air/water interface plays a decisive role in many natural processes. Several studies have reported that larger ions tend to be surface-active, implying ions are located on top of the water surface, thereby inducing electric fields that determine the interfacial water structure. Here we challenge this view by combining surface-specific heterodyne-detected vibrational sum-frequency generation with neural network-assisted ab initio molecular dynamics simulations. Our results show that ions in typical electrolyte solutions are, in fact, located in a subsurface region, leading to a stratification of such interfaces into two distinctive water layers. The outermost surface is ion-depleted, and the subsurface layer is ion-enriched. This surface stratification is a key element in explaining the ion-induced water reorganization at the outermost air/water interface.
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Affiliation(s)
- Yair Litman
- Max Planck Institute for Polymer Research, Mainz, Germany.
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
| | | | - Takakazu Seki
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Yuki Nagata
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Mischa Bonn
- Max Planck Institute for Polymer Research, Mainz, Germany.
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2
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Coste A, Slejko E, Zavadlav J, Praprotnik M. Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media. J Chem Theory Comput 2024; 20:411-420. [PMID: 38118122 PMCID: PMC10782447 DOI: 10.1021/acs.jctc.3c00984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/22/2023]
Abstract
Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.
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Affiliation(s)
- Amaury Coste
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
| | - Ema Slejko
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
- Department
of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana SI-1000, Slovenia
| | - Julija Zavadlav
- Professorship
of Multiscale Modeling of Fluid Materials, TUM School of Engineering
and Design, Technical University of Munich, Garching Near Munich DE-85748, Germany
| | - Matej Praprotnik
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
- Department
of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana SI-1000, Slovenia
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3
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Schaack S, Mangaud E, Fallacara E, Huppert S, Depondt P, Finocchi F. When Quantum Fluctuations Meet Structural Instabilities: The Isotope- and Pressure-Induced Phase Transition in the Quantum Paraelectric NaOH. PHYSICAL REVIEW LETTERS 2023; 131:126101. [PMID: 37802932 DOI: 10.1103/physrevlett.131.126101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/17/2023] [Accepted: 08/15/2023] [Indexed: 10/08/2023]
Abstract
Anhydrous sodium hydroxide, a common and structurally simple compound, shows spectacular isotope effects: NaOD undergoes a first-order transition, which is absent in NaOH. By combining ab initio electronic structure calculations with Feynman path integrals, we show that NaOH is an unusual example of a quantum paraelectric: zero-point quantum fluctuations stretch the weak hydrogen bonds (HBs) into a region where they are unstable and break. By strengthening the HBs via isotope substitution or applied pressure, the system can be driven to a broken-symmetry antiferroelectric phase. In passing, we provide a simple quantitative criterion for HB breaking in layered crystals and show that nuclear quantum effects are crucial in paraelectric to ferroelectric transitions in hydrogen-bonded hydroxides.
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Affiliation(s)
- Sofiane Schaack
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
| | - Etienne Mangaud
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
- Univ Gustave Eiffel, Univ Paris Est Creteil, CNRS, UMR 8208, MSME, F-77454 Marne-la-Vallée, France
| | - Erika Fallacara
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
| | - Simon Huppert
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
| | - Philippe Depondt
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
| | - Fabio Finocchi
- Sorbonne Université, CNRS UMR 7588, Institut des NanoSciences de Paris, INSP, 75005 Paris, France
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4
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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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5
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Bocus M, Goeminne R, Lamaire A, Cools-Ceuppens M, Verstraelen T, Van Speybroeck V. Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics. Nat Commun 2023; 14:1008. [PMID: 36823162 PMCID: PMC9950054 DOI: 10.1038/s41467-023-36666-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 μs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.
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Affiliation(s)
- Massimo Bocus
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium
| | - Ruben Goeminne
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium
| | - Aran Lamaire
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium
| | - Maarten Cools-Ceuppens
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium
| | - Toon Verstraelen
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium
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6
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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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7
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Fabregat R, Fabrizio A, Engel EA, Meyer B, Juraskova V, Ceriotti M, Corminboeuf C. Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides. J Chem Theory Comput 2022; 18:1467-1479. [PMID: 35179897 PMCID: PMC8908737 DOI: 10.1021/acs.jctc.1c00813] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
The application of
machine learning to theoretical chemistry has
made it possible to combine the accuracy of quantum chemical energetics
with the thorough sampling of finite-temperature fluctuations. To
reach this goal, a diverse set of methods has been proposed, ranging
from simple linear models to kernel regression and highly nonlinear
neural networks. Here we apply two widely different approaches to
the same, challenging problem: the sampling of the conformational
landscape of polypeptides at finite temperature. We develop a local
kernel regression (LKR) coupled with a supervised sparsity method
and compare it with a more established approach based on Behler-Parrinello
type neural networks. In the context of the LKR, we discuss how the
supervised selection of the reference pool of environments is crucial
to achieve accurate potential energy surfaces at a competitive computational
cost and leverage the locality of the model to infer which chemical
environments are poorly described by the DFTB baseline. We then discuss
the relative merits of the two frameworks and perform Hamiltonian-reservoir
replica-exchange Monte Carlo sampling and metadynamics simulations,
respectively, to demonstrate that both frameworks can achieve converged
and transferable sampling of the conformational landscape of complex
and flexible biomolecules with comparable accuracy and computational
cost.
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Affiliation(s)
| | | | - Edgar A Engel
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | | | | | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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8
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Gallegos M, Guevara-Vela JM, Pendás ÁM. NNAIMQ: A neural network model for predicting QTAIM charges. J Chem Phys 2022; 156:014112. [PMID: 34998318 DOI: 10.1063/5.0076896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Atomic charges provide crucial information about the electronic structure of a molecular system. Among the different definitions of these descriptors, the one proposed by the Quantum Theory of Atoms in Molecules (QTAIM) is particularly attractive given its invariance against orbital transformations although the computational cost associated with their calculation limits its applicability. Given that Machine Learning (ML) techniques have been shown to accelerate orders of magnitude the computation of a number of quantum mechanical observables, in this work, we take advantage of ML knowledge to develop an intuitive and fast neural network model (NNAIMQ) for the computation of QTAIM charges for C, H, O, and N atoms with high accuracy. Our model has been trained and tested using data from quantum chemical calculations in more than 45 000 molecular environments of the near-equilibrium CHON chemical space. The reliability and performance of NNAIMQ have been analyzed in a variety of scenarios, from equilibrium geometries to molecular dynamics simulations. Altogether, NNAIMQ yields remarkably small prediction errors, well below the 0.03 electron limit in the general case, while accelerating the calculation of QTAIM charges by several orders of magnitude.
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Affiliation(s)
- Miguel Gallegos
- Depto. Química Física y Analítica, Universidad de Oviedo, 33006 Oviedo, Spain
| | - José Manuel Guevara-Vela
- Institute of Chemistry, National Autonomous University of Mexico, Circuito Exterior, Ciudad Universitaria, Delegación Coyoacán, Mexico City C.P. 04510, Mexico
| | - Ángel Martín Pendás
- Depto. Química Física y Analítica, Universidad de Oviedo, 33006 Oviedo, Spain
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9
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Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis. Catalysts 2022. [DOI: 10.3390/catal12010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction—namely, the metal–gas, metal–liquid, and metal–support interfaces. Indeed, it is of considerable interest to know how a metal catalyst surface responds to gas or liquid adsorption under reaction conditions, and how its structure and catalytic properties evolve as a function of its interaction with the support. This short review aims to offer the reader a birds-eye view of state-of-the-art methods that enable more realistic simulation of dynamical phenomena at nanostructured interfaces by exploiting resource-efficient methods and/or the development of computational hardware and software.
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10
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Landeros-Rivera B, Gallegos M, Munarriz J, Laplaza R, Contreras García J. New venues in electron density analysis. Phys Chem Chem Phys 2022; 24:21538-21548. [DOI: 10.1039/d2cp01517j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We provide a comprehensive overview of the chemical information within the electron density: how to extract information, but also how to obtain and how to assess the quality of the...
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11
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Asselman K, Pellens N, Radhakrishnan S, Chandran CV, Martens JA, Taulelle F, Verstraelen T, Hellström M, Breynaert E, Kirschhock CEA. Super-ions of sodium cations with hydrated hydroxide anions: inorganic structure-directing agents in zeolite synthesis. MATERIALS HORIZONS 2021; 8:2576-2583. [PMID: 34870303 DOI: 10.1039/d1mh00733e] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
In inorganic zeolite formation, a direct correspondence between liquid state species in the synthesis and the supramolecular decoration of the pores in the as-made final zeolite has never been reported. In this paper, a direct link between the sodium speciation in the synthesis mixture and the pore structure and content of the final zeolite is demonstrated in the example of hydroxysodalite. Super-ions with 4 sodium cations bound by mono- and bihydrated hydroxide are identified as structure-directing agents for the formation of this zeolite. This documentation of inorganic solution species acting as a templating agent in zeolite formation opens new horizons for zeolite synthesis by design.
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Affiliation(s)
- Karel Asselman
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
| | - Nick Pellens
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
| | - Sambhu Radhakrishnan
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
- NMRCoRe, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
| | - C Vinod Chandran
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
- NMRCoRe, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
| | - Johan A Martens
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
- NMRCoRe, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
| | - Francis Taulelle
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
- NMRCoRe, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
| | - Toon Verstraelen
- Center for Molecular Modelling (CMM), Ghent University, Technologiepark 903, B-9052 Ghent, Belgium
| | - Matti Hellström
- Software for Chemistry and Materials B.V., 1081HV Amsterdam, The Netherlands
| | - Eric Breynaert
- COK-Kat, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium.
- NMRCoRe, KU Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
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12
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Unke O, Chmiela S, Sauceda HE, Gastegger M, Poltavsky I, Schütt KT, Tkatchenko A, Müller KR. Machine Learning Force Fields. Chem Rev 2021; 121:10142-10186. [PMID: 33705118 PMCID: PMC8391964 DOI: 10.1021/acs.chemrev.0c01111] [Citation(s) in RCA: 371] [Impact Index Per Article: 123.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Indexed: 12/27/2022]
Abstract
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.
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Affiliation(s)
- Oliver
T. Unke
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- DFG
Cluster of Excellence “Unifying Systems in Catalysis”
(UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Stefan Chmiela
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Huziel E. Sauceda
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- BASLEARN,
BASF-TU Joint Lab, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Michael Gastegger
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- DFG
Cluster of Excellence “Unifying Systems in Catalysis”
(UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
- BASLEARN,
BASF-TU Joint Lab, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Igor Poltavsky
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Kristof T. Schütt
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- BIFOLD−Berlin
Institute for the Foundations of Learning and Data, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
- Max Planck
Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- Google
Research, Brain Team, Berlin, Germany
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13
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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14
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Graham TR, Dembowski M, Wang HW, Mergelsberg ST, Nienhuis ET, Reynolds JG, Delegard CH, Wei Y, Snyder M, Leavy II, Baum SR, Fountain MS, Clark SB, Rosso KM, Pearce CI. Hydroxide promotes ion pairing in the NaNO 2-NaOH-H 2O system. Phys Chem Chem Phys 2021; 23:112-122. [PMID: 33305779 DOI: 10.1039/d0cp04799f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Nitrite (NO2-) is a prevalent nitrogen oxyanion in environmental and industrial processes, but its behavior in solution, including ion pair formation, is complex. This solution phase complexity impacts industries such as nuclear waste treatment, where NO2- significantly affects the solubility of other constituents present in sodium hydroxide (NaOH)-rich nuclear waste. This work provides molecular scale information into sodium nitrite (NaNO2) and NaOH ion-pairing processes to provide a physical basis for later development of thermodynamic models. Solubility isotherms of NaNO2 in aqueous mixtures with NaOH and total alkalinity were also measured. Spectroscopic characterization of these solutions utilized high-field nuclear magnetic resonance spectroscopy (NMR) and Raman spectroscopy, with additional solution structure detailed by X-ray total scattering pairwise distribution function analysis (X-ray PDF). Despite the NO2- deformation Raman band's insensitivity to added NaOH in saturated NaNO2 solutions, 23Na and 15N NMR studies indicated the Na+ and NO2- chemical environments change likely due to ion pairing. The ion pairing correlates with a decrease in diffusion coefficient of solution species as measured by pulsed field gradient 23Na and 1H NMR. Two-dimensional correlation analyses of the 2800-4000 cm-1 Raman region and X-ray PDF indicated that saturated NaNO2 and NaOH mixtures disrupt the hydrogen network of water into a new structure where the length of the OO correlations is contracted relative to the typical H2O structure. Beyond describing the solubility of NaNO2 in a multicomponent electrolyte mixture, these results also indicate that nitrite exhibits greater ion pairing in mixtures of concentrated NaNO2 and NaOH than in comparable solutions with only NaNO2.
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Affiliation(s)
- Trent R Graham
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
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15
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Rossi K, Jurásková V, Wischert R, Garel L, Corminbœuf C, Ceriotti M. Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH 3SO 3H and H 2O 2 in Phenol. J Chem Theory Comput 2020; 16:5139-5149. [PMID: 32567854 DOI: 10.1021/acs.jctc.0c00362] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects.
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Affiliation(s)
- Kevin Rossi
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Jurásková
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raphael Wischert
- Eco-Efficient Products and Processes Laboratory, Solvay, RIC Shanghai, Shanghai 201108, China
| | - Laurent Garel
- Aroma Performance Laboratory, Solvay, RIC Lyon, 69190 Saint-Fons, France
| | - Clémence Corminbœuf
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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16
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Misawa M, Fukushima S, Koura A, Shimamura K, Shimojo F, Tiwari S, Nomura KI, Kalia RK, Nakano A, Vashishta P. Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials. J Phys Chem Lett 2020; 11:4536-4541. [PMID: 32443935 DOI: 10.1021/acs.jpclett.0c00637] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.
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Affiliation(s)
- Masaaki Misawa
- Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Akihide Koura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Subodh Tiwari
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Rajiv K Kalia
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
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17
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Shao Y, Hellström M, Mitev PD, Knijff L, Zhang C. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials. J Chem Inf Model 2020; 60:1184-1193. [DOI: 10.1021/acs.jcim.9b00994] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Yunqi Shao
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Matti Hellström
- Software for Chemistry and Materials B.V., De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Pavlin D. Mitev
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Lisanne Knijff
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Chao Zhang
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
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18
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Shao Y, Hellström M, Yllö A, Mindemark J, Hermansson K, Behler J, Zhang C. Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions. Phys Chem Chem Phys 2020; 22:10426-10430. [PMID: 31895378 DOI: 10.1039/c9cp06479f] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Alkaline electrolyte solutions are important components in rechargeable batteries and alkaline fuel cells. As the ionic conductivity is thought to be a limiting factor in the performance of these devices, which are often operated at elevated temperatures, its temperature dependence is of significant interest. Here we use NaOH as a prototypical example of alkaline electrolytes, and for this system we have carried out reactive molecular dynamics simulations with an experimentally verified high-dimensional neural network potential derived from density-functional theory calculations. It is found that in concentrated NaOH solutions elevated temperatures enhance both the contributions of proton transfer to the ionic conductivity and deviations from the Nernst-Einstein relation. These findings are expected to be of practical relevance for electrochemical devices based on alkaline electrolyte solutions.
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Affiliation(s)
- Yunqi Shao
- Department of Chemistry -Ångström Laboratory, Uppsala University, Box 538, 751 21 Uppsala, Sweden.
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19
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Tabacchi G, Fabbiani M, Mino L, Martra G, Fois E. The Case of Formic Acid on Anatase TiO 2 (101): Where is the Acid Proton? Angew Chem Int Ed Engl 2019; 58:12431-12434. [PMID: 31310450 DOI: 10.1002/anie.201906709] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Indexed: 01/20/2023]
Abstract
Carboxylic-acid adsorption on anatase TiO2 is a relevant process in many technological applications. Yet, despite several decades of investigations, the acid-proton localization-either on the molecule or on the surface-is still an open issue. By modeling the adsorption of formic acid on top of anatase(101) surfaces, we highlight the formation of a short strong hydrogen bond. In the 0 K limit, the acid-proton behavior is ruled by quantum delocalization effects in a single potential well, while at ambient conditions, the proton undergoes a rapid classical shuttling in a shallow two-well free-energy profile. This picture, supported by agreement with available experiments, shows that the anatase surface acts like a protecting group for the carboxylic acid functionality. Such a new conceptual insight might help rationalize chemical processes involving carboxylic acids on oxide surfaces.
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Affiliation(s)
- Gloria Tabacchi
- Department of Science and High Technology, University of Insubria and INSTM, via Valleggio 9, I-22100, Como, Italy
| | - Marco Fabbiani
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centre, University of Torino, via P. Giuria 7, I-10125, Torino, Italy
| | - Lorenzo Mino
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centre, University of Torino, via P. Giuria 7, I-10125, Torino, Italy
| | - Gianmario Martra
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centre, University of Torino, via P. Giuria 7, I-10125, Torino, Italy
| | - Ettore Fois
- Department of Science and High Technology, University of Insubria and INSTM, via Valleggio 9, I-22100, Como, Italy
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20
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Tabacchi G, Fabbiani M, Mino L, Martra G, Fois E. The Case of Formic Acid on Anatase TiO
2
(101): Where is the Acid Proton? Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201906709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Gloria Tabacchi
- Department of Science and High TechnologyUniversity of Insubria and INSTM via Valleggio 9 I-22100 Como Italy
| | - Marco Fabbiani
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centreUniversity of Torino via P. Giuria 7 I-10125 Torino Italy
| | - Lorenzo Mino
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centreUniversity of Torino via P. Giuria 7 I-10125 Torino Italy
| | - Gianmario Martra
- Department of Chemistry and Nanostructured Interfaces and Surfaces NIS interdepartmental centreUniversity of Torino via P. Giuria 7 I-10125 Torino Italy
| | - Ettore Fois
- Department of Science and High TechnologyUniversity of Insubria and INSTM via Valleggio 9 I-22100 Como Italy
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21
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Abstract
Abstract
Confinement of molecules in one dimensional arrays of channel-shaped cavities has led to technologically interesting materials. However, the interactions governing the supramolecular aggregates still remain obscure, even for the most common guest molecule: water. Herein, we use computational chemistry methods (#compchem) to study the water organization inside two different channel-type environments: zeolite L – a widely used matrix for inclusion of dye molecules, and ZLMOF – the closest metal-organic-framework mimic of zeolite L. In ZLMOF, the methyl groups of the ligands protrude inside the channels, creating nearly isolated nanocavities. These cavities host well-separated ring-shaped clusters of water molecules, dominated mainly by water-water hydrogen bonds. ZLMOF provides arrays of “isolated supramolecule” environments, which might be exploited for the individual confinement of small species with interesting optical or catalytic properties. In contrast, the one dimensional channels of zeolite L contain a continuous supramolecular structure, governed by the water interactions with potassium cations and by water-water hydrogen bonds. Water imparts a significant energetic stabilization to both materials, which increases with the water content in ZLMOF and follows the opposite trend in zeolite L. The water network in zeolite L contains an intriguing hypercoordinated structure, where a water molecule is surrounded by five strong hydrogen bonds. Such a structure, here described for the first time in zeolites, can be considered as a water pre-dissociation complex and might explain the experimentally detected high proton activity in zeolite L nanochannels.
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Affiliation(s)
- Ettore Fois
- Department of Science and High Technology and INSTM , Università degli Studi dell’Insubria , Via Valleggio 11 , I-22100 Como , Italy
| | - Gloria Tabacchi
- Department of Science and High Technology and INSTM , Università degli Studi dell’Insubria , Via Valleggio 11 , I-22100 Como , Italy
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22
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Jackson NE, Webb MA, de Pablo JJ. Recent advances in machine learning towards multiscale soft materials design. Curr Opin Chem Eng 2019. [DOI: 10.1016/j.coche.2019.03.005] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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