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

Zhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, Weinan

doi:10.1103/physrevlett.120.143001

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

Abstract

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

Authors:

Zhang, Linfeng ^[1]; Han, Jiequn ^[1]; Wang, Han ^[2]; Car, Roberto ^[3]; E, Weinan ^[4]

Princeton Univ., NJ (United States). Program in Applied and Computational Mathematics
Inst. of Applied Physics and Computational Mathematics, Beijing (China); CAEP Software Center for High Performance Numerical Simulation, Beijing (China)
Princeton Univ., NJ (United States). Dept. of Chemistry. Dept. of Physics. Program in Applied and Computational Mathematics. Princeton Inst. for the Science and Technology of Materials
Princeton Univ., NJ (United States). Dept. of Mathematics. Program in Applied and Computational Mathematics; Peking Univ., Beijing (China). Beijing Inst. of Big Data Research. Center for Data Science. Beijing International Center for Mathematical Research

Publication Date:: Wed Apr 04 00:00:00 EDT 2018

Research Org.:: Princeton Univ., NJ (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1541298

Alternate Identifier(s):: OSTI ID: 1431394

Grant/Contract Number:: SC0008626; SC0009248

Resource Type:: Accepted Manuscript

Journal Name:: Physical Review Letters

Additional Journal Information:: Journal Volume: 120; Journal Issue: 14; Journal ID: ISSN 0031-9007

Publisher:: American Physical Society (APS)

Country of Publication:: United States

Language:: English

Subject:: 74 ATOMIC AND MOLECULAR PHYSICS

Citation Formats


                    Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, and E, Weinan. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.  United States: N. p., 2018. 
Web.  doi:10.1103/physrevlett.120.143001.

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                    Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, & E, Weinan. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.  United States.  https://doi.org/10.1103/physrevlett.120.143001

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                    Zhang, Linfeng, Han, Jiequn, Wang, Han, Car, Roberto, and E, Weinan. Wed .  
"Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics".  United States.  https://doi.org/10.1103/physrevlett.120.143001.  https://www.osti.gov/servlets/purl/1541298.

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@article{osti_1541298,

  title        = {Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics},

  author       = {Zhang, Linfeng and Han, Jiequn and Wang, Han and Car, Roberto and E, Weinan},

  abstractNote = {We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.},

  doi          = {10.1103/physrevlett.120.143001},

  journal      = {Physical Review Letters},

  number       = 14,

  volume       = 120,

  place        = {United States},

  year         = {Wed Apr 04 00:00:00 EDT 2018},

  month        = {Wed Apr 04 00:00:00 EDT 2018}

}

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Jackson, Nicholas E.; Bowen, Alec S.; Antony, Lucas W.
Science Advances, Vol. 5, Issue 3
DOI: 10.1126/sciadv.aav1190

A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity
journal, May 2019

Lunghi, Alessandro; Sanvito, Stefano
Science Advances, Vol. 5, Issue 5
DOI: 10.1126/sciadv.aaw2210

N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
preprint, January 2018

Kondor, Risi
arXiv
DOI: 10.48550/arxiv.1803.01588

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

Abstract

Citation Formats

Generalized Gradient Approximation Made Simple journal, October 1996

Accurate determination of crystal structures based on averaged local bond order parameters journal, September 2008

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning journal, January 2012

A universal strategy for the creation of machine learning-based atomistic force fields journal, September 2017

Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids journal, January 1996

Nosé–Hoover chains: The canonical ensemble via continuous dynamics journal, August 1992

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost journal, January 2017

How van der Waals interactions determine the unique properties of water journal, July 2016

Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals journal, June 1984

Toward reliable density functional methods without adjustable parameters: The PBE0 model journal, April 1999

ReaxFF: A Reactive Force Field for Hydrocarbons journal, October 2001

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal, April 2007

Comparison of simple potential functions for simulating liquid water journal, July 1983

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials journal, March 2015

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data journal, February 2009

i-PI: A Python interface for ab initio path integral molecular dynamics simulations journal, March 2014

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit journal, February 2013

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics journal, July 2018

CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields journal, January 2009

Quantum Monte Carlo journal, February 1986

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons journal, April 2010

Perspective: Machine learning potentials for atomistic simulations journal, November 2016

Quantum-chemical insights from deep tensor neural networks journal, January 2017

Development and testing of a general amber force field journal, January 2004

Coupled‐cluster methods with noniterative triple excitations for restricted open‐shell Hartree–Fock and other general single determinant reference functions. Energies and analytical gradients journal, June 1993

Accelerating the convergence of path integral dynamics with a generalized Langevin equation journal, February 2011

Machine learning of accurate energy-conserving molecular force fields journal, May 2017

Crystal Structure and Pair Potentials: A Molecular-Dynamics Study journal, October 1980

Advanced capabilities for materials modelling with Quantum ESPRESSO journal, October 2017

Self-Consistent Equations Including Exchange and Correlation Effects journal, November 1965

A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons journal, January 2002

Machine learning of accurate energy-conserving molecular force fields text, January 2017

Advanced capabilities for materials modelling with Quantum ESPRESSO text, January 2017

Fast and accurate modeling of molecular atomization energies with machine learning text, January 2012

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost text, January 2017

Accurate determination of crystal structures based on averaged local bond order parameters text, January 2008

Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons text, January 2009

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning text, January 2011

i-PI: A Python interface for ab initio path integral molecular dynamics simulations text, January 2014

Quantum-Chemical Insights from Deep Tensor Neural Networks text, January 2016

Machine Learning of Accurate Energy-Conserving Molecular Force Fields text, January 2016

Advanced capabilities for materials modelling with Quantum ESPRESSO text, January 2017

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics text, January 2017

A Critical Review of Machine Learning of Energy Materials journal, January 2020

Role of Water in the Reaction Mechanism and endo / exo Selectivity of 1,3‐Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning journal, May 2019

Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme journal, December 2019

Towards exact molecular dynamics simulations with machine-learned force fields journal, September 2018

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials journal, June 2019

Recent advances and applications of machine learning in solid-state materials science journal, August 2019

Fast, accurate, and transferable many-body interatomic potentials by symbolic regression journal, November 2019

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems journal, September 2019

DeePCG: Constructing coarse-grained models via deep neural networks journal, July 2018

Adaptive coupling of a deep neural network potential to a classical force field journal, October 2018

Analysis of trajectory similarity and configuration similarity in on-the-fly surface-hopping simulation on multi-channel nonadiabatic photoisomerization dynamics journal, December 2018

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces journal, March 2019

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules journal, April 2019

Atom-density representations for machine learning journal, April 2019

Deep learning inter-atomic potential model for accurate irradiation damage simulations journal, June 2019

Evaluation of experimental alkali metal ion–ligand noncovalent bond strengths with DLPNO-CCSD(T) method journal, July 2019

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences journal, August 2019

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag 2 Se journal, September 2019

Machine learning for interatomic potential models journal, February 2020

FCHL revisited: Faster and more accurate quantum machine learning journal, January 2020

Incorporating long-range physics in atomic-scale machine learning journal, November 2019

Uniformly accurate machine learning-based hydrodynamic models for kinetic equations journal, October 2019

Isotope effects in liquid water via deep potential molecular dynamics journal, October 2019

Machine learning and molecular design of self-assembling -conjugated oligopeptides journal, April 2018

From DFT to machine learning: recent approaches to materials science–a review journal, May 2019

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds journal, November 2019

Constructing convex energy landscapes for atomistic structure optimization journal, December 2019

Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies journal, July 2019

Atomic energy mapping of neural network potential journal, September 2019

Electronic structure at coarse-grained resolutions from supervised machine learning journal, March 2019

A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity journal, May 2019

High-repetition-rate femtosecond mid-infrared pulses generated by nonlinear optical modulation of continuous-wave QCLs and ICLs journal, January 2019

De novo exploration and self-guided learning of potential-energy surfaces text, January 2019

Molecular Modeling Investigations of Sorption and Diffusion of Small Molecules in Glassy Polymers journal, August 2019

Generalized Gradient Approximation Made Simple
journal, October 1996

Accurate determination of crystal structures based on averaged local bond order parameters
journal, September 2008

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012

A universal strategy for the creation of machine learning-based atomistic force fields
journal, September 2017

Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids
journal, January 1996

Nosé–Hoover chains: The canonical ensemble via continuous dynamics
journal, August 1992

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
journal, January 2017

How van der Waals interactions determine the unique properties of water
journal, July 2016

Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
journal, June 1984

Toward reliable density functional methods without adjustable parameters: The PBE0 model
journal, April 1999

ReaxFF: A Reactive Force Field for Hydrocarbons
journal, October 2001

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007

Comparison of simple potential functions for simulating liquid water
journal, July 1983

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
journal, March 2015

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
journal, February 2009

i-PI: A Python interface for ab initio path integral molecular dynamics simulations
journal, March 2014

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
journal, February 2013

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
journal, July 2018

CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields
journal, January 2009

Quantum Monte Carlo
journal, February 1986

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010

Perspective: Machine learning potentials for atomistic simulations
journal, November 2016

Quantum-chemical insights from deep tensor neural networks
journal, January 2017

Development and testing of a general amber force field
journal, January 2004

Coupled‐cluster methods with noniterative triple excitations for restricted open‐shell Hartree–Fock and other general single determinant reference functions. Energies and analytical gradients
journal, June 1993

Accelerating the convergence of path integral dynamics with a generalized Langevin equation
journal, February 2011

Machine learning of accurate energy-conserving molecular force fields
journal, May 2017

Crystal Structure and Pair Potentials: A Molecular-Dynamics Study
journal, October 1980

Advanced capabilities for materials modelling with Quantum ESPRESSO
journal, October 2017

Self-Consistent Equations Including Exchange and Correlation Effects
journal, November 1965

A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons
journal, January 2002

Machine learning of accurate energy-conserving molecular force fields
text, January 2017

Advanced capabilities for materials modelling with Quantum ESPRESSO
text, January 2017

Fast and accurate modeling of molecular atomization energies with machine learning
text, January 2012

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
text, January 2017

Accurate determination of crystal structures based on averaged local bond order parameters
text, January 2008

Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
text, January 2009

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
text, January 2011

i-PI: A Python interface for ab initio path integral molecular dynamics simulations
text, January 2014

Quantum-Chemical Insights from Deep Tensor Neural Networks
text, January 2016

Machine Learning of Accurate Energy-Conserving Molecular Force Fields
text, January 2016

Advanced capabilities for materials modelling with Quantum ESPRESSO
text, January 2017

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
text, January 2017

A Critical Review of Machine Learning of Energy Materials
journal, January 2020

Role of Water in the Reaction Mechanism and endo / exo Selectivity of 1,3‐Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning
journal, May 2019

Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme
journal, December 2019

Towards exact molecular dynamics simulations with machine-learned force fields
journal, September 2018

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
journal, June 2019

Recent advances and applications of machine learning in solid-state materials science
journal, August 2019

Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
journal, November 2019

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
journal, September 2019

DeePCG: Constructing coarse-grained models via deep neural networks
journal, July 2018

Adaptive coupling of a deep neural network potential to a classical force field
journal, October 2018

Analysis of trajectory similarity and configuration similarity in on-the-fly surface-hopping simulation on multi-channel nonadiabatic photoisomerization dynamics
journal, December 2018

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
journal, March 2019

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
journal, April 2019

Atom-density representations for machine learning
journal, April 2019

Deep learning inter-atomic potential model for accurate irradiation damage simulations
journal, June 2019

Evaluation of experimental alkali metal ion–ligand noncovalent bond strengths with DLPNO-CCSD(T) method
journal, July 2019

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
journal, August 2019

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag ₂ Se
journal, September 2019

Machine learning for interatomic potential models
journal, February 2020

FCHL revisited: Faster and more accurate quantum machine learning
journal, January 2020

Incorporating long-range physics in atomic-scale machine learning
journal, November 2019

Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
journal, October 2019

Isotope effects in liquid water via deep potential molecular dynamics
journal, October 2019

Machine learning and molecular design of self-assembling -conjugated oligopeptides
journal, April 2018

From DFT to machine learning: recent approaches to materials science–a review
journal, May 2019

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds
journal, November 2019

Constructing convex energy landscapes for atomistic structure optimization
journal, December 2019

Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies
journal, July 2019

Atomic energy mapping of neural network potential
journal, September 2019

Electronic structure at coarse-grained resolutions from supervised machine learning
journal, March 2019

A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity
journal, May 2019

High-repetition-rate femtosecond mid-infrared pulses generated by nonlinear optical modulation of continuous-wave QCLs and ICLs
journal, January 2019

De novo exploration and self-guided learning of potential-energy surfaces
text, January 2019

Molecular Modeling Investigations of Sorption and Diffusion of Small Molecules in Glassy Polymers
journal, August 2019

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Learning from the density to correct total energy and forces in first principle simulations
journal, October 2019

Machine Learning a General-Purpose Interatomic Potential for Silicon
text, January 2018