Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Torres, Alberto; Pedroza, Luana S.; Fernandez-Serra, Marivi; Rocha, Alexandre R.

doi:10.1021/acs.jpcb.1c04372

Title: Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Abstract

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

Authors:

Torres, Alberto ^[1]; Pedroza, Luana S. ^[2];

^[3];

^[1]

São Paulo State University (UNESP) (Brazil)
Universidade Federal do ABC, Santo Andre (Brazil)
State Univ. of New York (SUNY), Stony Brook, NY (United States)

Publication Date:: Mon Sep 20 00:00:00 EDT 2021

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

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES); São Paulo Research Foundation (FAPESP)

OSTI Identifier:: 1994969

Grant/Contract Number:: SC0019394; SC0001137; FAPESP 2017/02317-2; 2016/01343-7; 2017/10292-0

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Physical Chemistry. B

Additional Journal Information:: Journal Volume: 125; Journal Issue: 38; Journal ID: ISSN 1520-6106

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats


                    Torres, Alberto, Pedroza, Luana S., Fernandez-Serra, Marivi, and Rocha, Alexandre R. Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water.  United States: N. p., 2021. 
Web.  doi:10.1021/acs.jpcb.1c04372.

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                    Torres, Alberto, Pedroza, Luana S., Fernandez-Serra, Marivi, & Rocha, Alexandre R. Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water.  United States.  https://doi.org/10.1021/acs.jpcb.1c04372

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                    Torres, Alberto, Pedroza, Luana S., Fernandez-Serra, Marivi, and Rocha, Alexandre R. Mon .  
"Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water".  United States.  https://doi.org/10.1021/acs.jpcb.1c04372.  https://www.osti.gov/servlets/purl/1994969.

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@article{osti_1994969,

  title        = {Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water},

  author       = {Torres, Alberto and Pedroza, Luana S. and Fernandez-Serra, Marivi and Rocha, Alexandre R.},

  abstractNote = {Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.},

  doi          = {10.1021/acs.jpcb.1c04372},

  journal      = {Journal of Physical Chemistry. B},

  number       = 38,

  volume       = 125,

  place        = {United States},

  year         = {Mon Sep 20 00:00:00 EDT 2021},

  month        = {Mon Sep 20 00:00:00 EDT 2021}

}

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Similar Records

Title: Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Abstract

Citation Formats

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

A comparative study of imaginary time path integral based methods for quantum dynamics journal, April 2006

Note: Assessment of the SCAN+rVV10 functional for the structure of liquid water journal, December 2017

Temperature and size dependence for Monte Carlo simulations of TIP4P water journal, December 1985

Isothermal-isobaric molecular dynamics using stochastic velocity rescaling journal, February 2009

Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids journal, April 1981

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Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional journal, July 2020

Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges journal, April 2016

Density, structure, and dynamics of water: The effect of van der Waals interactions journal, January 2011

Machine learning accurate exchange and correlation functionals of the electronic density journal, July 2020

Optimal finite-range atomic basis sets for liquid water and ice journal, October 2013

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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional journal, December 2020

Simulation of quantum zero-point effects in water using a frequency-dependent thermostat journal, April 2013

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

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

Force Field for Water Based on Neural Network journal, May 2018

Comparison of path integral molecular dynamics methods for the infrared absorption spectrum of liquid water journal, August 2008

Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals journal, August 1993

Optimization of an exchange-correlation density functional for water journal, June 2016

Ab initio theory and modeling of water journal, September 2017

The structure of water around the compressibility minimum journal, December 2014

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications journal, September 2020

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

Exchange functional that tests the robustness of the plasmon description of the van der Waals density functional journal, January 2014

Network equilibration and first-principles liquid water journal, January 2004

The Radial Distribution Functions of Water as Derived from Radiation Total Scattering Experiments: Is There Anything We Can Say for Sure? journal, January 2013

The SIESTA method for ab initio order- N materials simulation journal, March 2002

Physics and chemistry of water and ice journal, January 2011

Polymorphic transitions in single crystals: A new molecular dynamics method journal, December 1981

Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements journal, January 2000

Bias-dependent local structure of water molecules at a metallic interface journal, January 2018

Anomalous Nuclear Quantum Effects in Ice journal, May 2012

Viscosity of liquid water in the range −8 °C to 150 °C journal, July 1978

Re-examining the properties of the aqueous vapor–liquid interface using dispersion corrected density functional theory journal, September 2011

Ensemble first-principles molecular dynamics simulations of water using the SCAN meta-GGA density functional journal, October 2019

Competing quantum effects in the dynamics of a flexible water model journal, July 2009

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water journal, October 2020

Approximation capabilities of multilayer feedforward networks journal, January 1991

Quantum Differences between Heavy and Light Water journal, August 2008

Perspective: How good is DFT for water? journal, April 2016

Structure and dynamics of aqueous solutions from PBE-based first-principles molecular dynamics simulations journal, October 2016

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens journal, June 2020

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

A comparative study of imaginary time path integral based methods for quantum dynamics
journal, April 2006

Note: Assessment of the SCAN+rVV10 functional for the structure of liquid water
journal, December 2017

Temperature and size dependence for Monte Carlo simulations of TIP4P water
journal, December 1985

Isothermal-isobaric molecular dynamics using stochastic velocity rescaling
journal, February 2009

Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids
journal, April 1981

Fast Parallel Algorithms for Short-Range Molecular Dynamics
journal, March 1995

Chemical accuracy for the van der Waals density functional
journal, December 2009

Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional
journal, July 2020

Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges
journal, April 2016

Density, structure, and dynamics of water: The effect of van der Waals interactions
journal, January 2011

Machine learning accurate exchange and correlation functionals of the electronic density
journal, July 2020

Optimal finite-range atomic basis sets for liquid water and ice
journal, October 2013

Water at charged interfaces
journal, June 2021

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional
journal, December 2020

Simulation of quantum zero-point effects in water using a frequency-dependent thermostat
journal, April 2013

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

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

Force Field for Water Based on Neural Network
journal, May 2018

Comparison of path integral molecular dynamics methods for the infrared absorption spectrum of liquid water
journal, August 2008

Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals
journal, August 1993

Optimization of an exchange-correlation density functional for water
journal, June 2016

Ab initio theory and modeling of water
journal, September 2017

The structure of water around the compressibility minimum
journal, December 2014

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications
journal, September 2020

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

Exchange functional that tests the robustness of the plasmon description of the van der Waals density functional
journal, January 2014

Network equilibration and first-principles liquid water
journal, January 2004

The Radial Distribution Functions of Water as Derived from Radiation Total Scattering Experiments: Is There Anything We Can Say for Sure?
journal, January 2013

The SIESTA method for ab initio order- N materials simulation
journal, March 2002

Physics and chemistry of water and ice
journal, January 2011

Polymorphic transitions in single crystals: A new molecular dynamics method
journal, December 1981

Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements
journal, January 2000

Bias-dependent local structure of water molecules at a metallic interface
journal, January 2018

Anomalous Nuclear Quantum Effects in Ice
journal, May 2012

Viscosity of liquid water in the range −8 °C to 150 °C
journal, July 1978

Re-examining the properties of the aqueous vapor–liquid interface using dispersion corrected density functional theory
journal, September 2011

Ensemble first-principles molecular dynamics simulations of water using the SCAN meta-GGA density functional
journal, October 2019

Competing quantum effects in the dynamics of a flexible water model
journal, July 2009

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water
journal, October 2020

Approximation capabilities of multilayer feedforward networks
journal, January 1991

Quantum Differences between Heavy and Light Water
journal, August 2008

Perspective: How good is DFT for water?
journal, April 2016

Structure and dynamics of aqueous solutions from PBE-based first-principles molecular dynamics simulations
journal, October 2016

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
journal, June 2020