Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method

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

(2021) Tsz Wai Ko et al.   Nature Communications

Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

(2021) Shi Jun Ang et al.   Chem

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials

(2021) Qidong Lin et al.   Journal of Chemical Theory and Computation

Machine Learning for Chemical Reactions

(2021) Markus Meuwly   CHEMICAL REVIEWS

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

(2020) Yuzhi Zhang et al.   COMPUTER PHYSICS COMMUNICATIONS

New Perspectives on CO2–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface

(2020) Marcos del Cueto et al.   Journal of Physical Chemistry C

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy

(2020) Qidong Lin et al.   JOURNAL OF CHEMICAL PHYSICS

Automated Construction of Potential Energy Surfaces Suitable to Describe van der Waals Complexes with Highly Excited Nascent Molecules: The Rotational Spectra of Ar–CS(v) and Ar–SiS(v)

(2020) Ernesto Quintas-Sánchez et al.   JOURNAL OF PHYSICAL CHEMISTRY A

A Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces

(2020) Simone Venturi et al.   JOURNAL OF PHYSICAL CHEMISTRY A

High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning

(2020) Bin Jiang et al.   Journal of Physical Chemistry Letters

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

(2020) Sergei Manzhos et al.   CHEMICAL REVIEWS

Dynamics of H2O Adsorption on Pt(110)-(1 × 2) Based on a Neural Network Potential Energy Surface

(2020) Ce Hu et al.   Journal of Physical Chemistry C

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

(2020) Bing Huang et al.   Nature Chemistry

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

(2020) Denghui Lu et al.   COMPUTER PHYSICS COMMUNICATIONS

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Parallel Multistream Training of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Theoretical Investigations of Rate Coefficients of H + H2O2 → OH + H2O on a Full-Dimensional Potential Energy Surface

(2019) Xiaoxiao Lu et al.   JOURNAL OF PHYSICAL CHEMISTRY A

PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces

(2019) Adam S. Abbott et al.   Journal of Chemical Theory and Computation

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

Permutationally Invariant Potential Energy Surfaces

(2018) Chen Qu et al.   Annual Review of Physical Chemistry

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

(2018) Arunkumar Chitteth Rajan et al.   CHEMISTRY OF MATERIALS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Atomic Energies from a Convolutional Neural Network

(2018) Xin Chen et al.   Journal of Chemical Theory and Computation

Force Field for Water Based on Neural Network

(2018) Hao Wang et al.   Journal of Physical Chemistry Letters

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

AUTOSURF: A Freely Available Program To Construct Potential Energy Surfaces

(2018) Ernesto Quintas-Sánchez et al.   Journal of Chemical Information and Modeling

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network

(2018) Hao Wang et al.   Journal of Chemical Theory and Computation

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

(2017) Peter Eastman et al.   PLoS Computational Biology

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

(2015) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

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

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Solution coating around ice particles of incipient cirrus clouds

(2013) A. Bogdan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer

(2013) Tobias Morawietz et al.   ZEITSCHRIFT FUR PHYSIKALISCHE CHEMIE-INTERNATIONAL JOURNAL OF RESEARCH IN PHYSICAL CHEMISTRY & CHEMICAL PHYSICS

A Load-Balancing Self-Organizing Incremental Neural Network

(2013) Hongwei Zhang et al.   IEEE Transactions on Neural Networks and Learning Systems

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Benchmark Structures and Binding Energies of Small Water Clusters with Anharmonicity Corrections

(2011) Berhane Temelso et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS