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Four Generations of High-Dimensional Neural Network Potentials

Chemical reviews, 2021-08, Vol.121 (16), p.10037-10072

Behler, Jörg

2021

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Perspective: Machine learning potentials for atomistic simulations

The Journal of chemical physics, 2016-11, Vol.145 (17), p.170901-170901

Behler, Jörg

2016

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Constructing high-dimensional neural network potentials: A tutorial review

International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1032-1050

Behler, Jörg

2015

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Atom-centered symmetry functions for constructing high-dimensional neural network potentials

The Journal of chemical physics, 2011-02, Vol.134 (7), p.074106-074106-13

Behler, Jörg

2011

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Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

Journal of chemical theory and computation, 2019-03, Vol.15 (3), p.1827-1840

2019

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A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2013-08, Vol.117 (32), p.7356-7366

2013

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Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground

Journal of chemical theory and computation, 2020-01, Vol.16 (1), p.88-99

2020

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Parallel Multistream Training of High-Dimensional Neural Network Potentials

Journal of chemical theory and computation, 2019-05, Vol.15 (5), p.3075-3092

2019

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From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF‑5

Journal of chemical theory and computation, 2019-06, Vol.15 (6), p.3793-3809

2019

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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Nature communications, 2021-01, Vol.12 (1), p.398-398, Article 398

2021

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Machine learning molecular dynamics for the simulation of infrared spectra

Chemical science (Cambridge), 2017-10, Vol.8 (10), p.6924-6935

2017

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How to train a neural network potential

The Journal of chemical physics, 2023-09, Vol.159 (12)

2023

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Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2020-07, Vol.124 (28), p.5737-5745

2020

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General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer

Accounts of chemical research, 2021-02, Vol.54 (4), p.808-817

2021

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Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)

The journal of physical chemistry letters, 2017-05, Vol.8 (10), p.2131-2136

2017

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Machine learning potentials for extended systems: a perspective

The European physical journal. B, Condensed matter physics, 2021-07, Vol.94 (7), Article 142

2021

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Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

The journal of physical chemistry letters, 2017-04, Vol.8 (7), p.1476-1483

2017

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How van der Waals interactions determine the unique properties of water

Proceedings of the National Academy of Sciences - PNAS, 2016-07, Vol.113 (30), p.8368-8373

2016

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Ab initio thermodynamics of liquid and solid water

Proceedings of the National Academy of Sciences - PNAS, 2019-01, Vol.116 (4), p.1110-1115

2019

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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241730-241730

2018

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A Hessian-based assessment of atomic forces for training machine learning interatomic potentials

The Journal of chemical physics, 2022-03, Vol.156 (11), p.114106-114106

2022

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Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations

Journal of physical chemistry. C, 2019-01, Vol.123 (2), p.1293-1304

2019

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Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Journal of chemical theory and computation, 2024-09, Vol.20 (18), p.8247-8260

2024

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Erratum: “Perspective: Machine learning potentials for atomistic simulations” [J. Chem. Phys. 145, 170901 (2016)]

The Journal of chemical physics, 2016-12, Vol.145 (21), p.219901-219901

Behler, Jörg

2016

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Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments

The journal of physical chemistry letters, 2016-06, Vol.7 (12), p.2210-2215

2016

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Four Generations of High-Dimensional Neural Network Potentials

Perspective: Machine learning potentials for atomistic simulations

Constructing high-dimensional neural network potentials: A tutorial review

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground

Parallel Multistream Training of High-Dimensional Neural Network Potentials

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF‑5

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Machine learning molecular dynamics for the simulation of infrared spectra

How to train a neural network potential

Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance

General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer

Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)

Machine learning potentials for extended systems: a perspective

Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

How van der Waals interactions determine the unique properties of water

Ab initio thermodynamics of liquid and solid water

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

A Hessian-based assessment of atomic forces for training machine learning interatomic potentials

Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Erratum: “Perspective: Machine learning potentials for atomistic simulations” [J. Chem. Phys. 145, 170901 (2016)]

Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments

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