A coarse-grained deep neural network model for liquid water

doi:10.1063/1.5116591

This content will become publicly available on November 4, 2020

Title: A coarse-grained deep neural network model for liquid water

Authors:

^[1]; Loeffler, Troy D. ^[1]; Chan, Henry ^[1];

^[1]; Narayanan, Badri ^[2]; Sankaranarayanan, Subramanian K. R. S. ^[3]

Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, USA
Department of Mechanical Engineering, University of Louisville, Louisville, Kentucky 40202, USA
Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, USA, Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, USA

Publication Date:: 2019-11-04

Sponsoring Org.:: USDOE

OSTI Identifier:: 1573079

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Applied Physics Letters

Additional Journal Information:: Journal Name: Applied Physics Letters Journal Volume: 115 Journal Issue: 19; Journal ID: ISSN 0003-6951

Publisher:: American Institute of Physics

Country of Publication:: United States

Language:: English

Citation Formats


                    Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. A coarse-grained deep neural network model for liquid water.  United States: N. p., 2019. 
        Web.  doi:10.1063/1.5116591.

Copy to clipboard


                    Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, & Sankaranarayanan, Subramanian K. R. S. A coarse-grained deep neural network model for liquid water.  United States.  doi:10.1063/1.5116591.

Copy to clipboard


                    Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. Mon .  
        "A coarse-grained deep neural network model for liquid water".  United States.  doi:10.1063/1.5116591.

Copy to clipboard


                    
@article{osti_1573079,

  title        = {A coarse-grained deep neural network model for liquid water},

  author       = {Patra, Tarak K. and Loeffler, Troy D. and Chan, Henry and Cherukara, Mathew J. and Narayanan, Badri and Sankaranarayanan, Subramanian K. R. S.},

  abstractNote = {},

  doi          = {10.1063/1.5116591},

  journal      = {Applied Physics Letters},

  number       = 19,

  volume       = 115,

  place        = {United States},

  year         = {2019},

  month        = {11}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

This content will become publicly available on November 4, 2020

Publisher's Version of Record

DOI: 10.1063/1.5116591

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

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

Holz, Manfred; Heil, Stefan R.; Sacco, Antonio
Physical Chemistry Chemical Physics, Vol. 2, Issue 20
DOI: 10.1039/b005319h

Machine learning coarse grained models for water
journal, January 2019

Chan, Henry; Cherukara, Mathew J.; Narayanan, Badri
Nature Communications, Vol. 10, Issue 1
DOI: 10.1038/s41467-018-08222-6

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

Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
npj Computational Materials, Vol. 5, Issue 1
DOI: 10.1038/s41524-019-0221-0

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
journal, January 2019

Chan, Henry; Narayanan, Badri; Cherukara, Mathew J.
The Journal of Physical Chemistry C, Vol. 123, Issue 12
DOI: 10.1021/acs.jpcc.8b09917

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
journal, June 2019

Cubuk, Ekin D.; Sendek, Austin D.; Reed, Evan J.
The Journal of Chemical Physics, Vol. 150, Issue 21
DOI: 10.1063/1.5093220

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

Zhang, Linfeng; Han, Jiequn; Wang, Han
The Journal of Chemical Physics, Vol. 149, Issue 3
DOI: 10.1063/1.5027645

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

Skinner, L. B.; Benmore, C. J.; Neuefeind, J. C.
The Journal of Chemical Physics, Vol. 141, Issue 21
DOI: 10.1063/1.4902412

A method for the solution of certain non-linear problems in least squares
journal, January 1944

Levenberg, Kenneth
Quarterly of Applied Mathematics, Vol. 2, Issue 2
DOI: 10.1090/qam/10666

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
journal, March 2016

Artrith, Nongnuch; Urban, Alexander
Computational Materials Science, Vol. 114
DOI: 10.1016/j.commatsci.2015.11.047

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

Behler, Jörg
The Journal of Chemical Physics, Vol. 145, Issue 17
DOI: 10.1063/1.4966192

A molecular level explanation of the density maximum of liquid water from computer simulations with a polarizable potential model
journal, February 2000

Jedlovszky, Pál; Mezei, Mihaly; Vallauri, Renzo
Chemical Physics Letters, Vol. 318, Issue 1-3
DOI: 10.1016/S0009-2614(00)00002-6

Physically informed artificial neural networks for atomistic modeling of materials
journal, May 2019

Pun, G. P. Purja; Batra, R.; Ramprasad, R.
Nature Communications, Vol. 10, Issue 1
DOI: 10.1038/s41467-019-10343-5

A survey of transfer learning
journal, May 2016

Weiss, Karl; Khoshgoftaar, Taghi M.; Wang, DingDing
Journal of Big Data, Vol. 3, Issue 1
DOI: 10.1186/s40537-016-0043-6

Construction of high-dimensional neural network potentials using environment-dependent atom pairs
journal, May 2012

Jose, K. V. Jovan; Artrith, Nongnuch; Behler, Jörg
The Journal of Chemical Physics, Vol. 136, Issue 19
DOI: 10.1063/1.4712397

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
journal, April 2013

Morawietz, Tobias; Behler, Jörg
The Journal of Physical Chemistry A, Vol. 117, Issue 32
DOI: 10.1021/jp401225b

Atom-centered symmetry functions for constructing high-dimensional neural network potentials
journal, February 2011

Behler, Jörg
The Journal of Chemical Physics, Vol. 134, Issue 7
DOI: 10.1063/1.3553717

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

Behler, Jörg; Parrinello, Michele
Physical Review Letters, Vol. 98, Issue 14
DOI: 10.1103/PhysRevLett.98.146401

Similar records in OSTI.GOV collections:

DeePCG: Constructing coarse-grained models via deep neural networks

Journal Article Zhang, Linfeng ; Han, Jiequn ; Wang, Han ; ... - Journal of Chemical Physics

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application, we consider liquid watermore »« less
Cited by 8
DOI: 10.1063/1.5027645

Full Text Available
Initial evaluation of profiles of temperature, water vapor, and cloud liquid water from a new microwave profiling radiometer.

Conference Liljegren, J. C. ; Lesht, B. M. ; Clothiaux, E. E. ; ...

To measure the vertical profiles of temperature and water vapor that are essential for modeling atmospheric processes, the Atmospheric Radiation Measurement (ARM) Program of the U. S. Department of Energy launches approximately 2600 radiosondes each year from its Southern Great Plains (SGP) facilities in Oklahoma and Kansas, USA. The annual cost of this effort exceeds $500,000 in materials and labor. Despite the expense, these soundings have a coarse temporal resolution and reporting interval compared with model time steps. In contrast, the radiation measurements used for model evaluations have temporal resolutions and reporting intervals of a few minutes at most. Conversely,more »« less
Full Text Available
Coarse-grained model of water diffusion and proton conductivity in hydrated polyelectrolyte membrane

Journal Article Lee, Ming-Tsung ; Vishnyakov, Aleksey ; Neimark, Alexander V., E-mail: aneimark@rutgers.edu - Journal of Chemical Physics

Using dissipative particle dynamics (DPD), we simulate nanoscale segregation, water diffusion, and proton conductivity in hydrated sulfonated polystyrene (sPS). We employ a novel model [Lee et al. J. Chem. Theory Comput. 11(9), 4395-4403 (2015)] that incorporates protonation/deprotonation equilibria into DPD simulations. The polymer and water are modeled by coarse-grained beads interacting via short-range soft repulsion and smeared charge electrostatic potentials. The proton is introduced as a separate charged bead that forms dissociable Morse bonds with the base beads representing water and sulfonate anions. Morse bond formation and breakup artificially mimics the Grotthuss mechanism of proton hopping between the bases. Themore »« less
DOI: 10.1063/1.4938271
Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models

Journal Article Loeffler, Troy D. ; Chan, Henry ; Narayanan, Badri ; ... - Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back-mapping, of coarse grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force-fields and often rely on running a long time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here,more »« less
DOI: 10.1021/acs.jpcb.8b01791

Full Text Available
Structural Correlations and Percolation in Twisted Perylene Diimides Using a Simple Anisotropic Coarse-Grained Model

Journal Article Bowen, Alec S. ; Jackson, Nicholas E. ; Reid, Daniel R. ; ... - Journal of Chemical Theory and Computation

Large, twisted, and fused conjugated molecular architectures have begun to appear more prominently in the organic semiconductor literature. From a modeling perspective, such structures present a challenge to conventional simulation techniques; atomistic resolutions are computationally inefficient, while traditional isotropic coarse-grained models do not capture the inherent anisotropies of the molecules. In this work, we develop a simple coarse-grained model that explicitly incorporates the anisotropy of these molecular architectures, thereby providing a route toward analyzing pi-stacking, and thus qualitative electronic structure, at a computationally efficient coarse-grained resolution. Our simple coarse-grained model maintains relative orientations of conjugated rings, as well as inter-ringmore »« less
DOI: 10.1021/acs.jctc.8b00742

Full Text Available

Similar Records

This content will become publicly available on November 4, 2020

Title: A coarse-grained deep neural network model for liquid water

Citation Formats

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

Machine learning coarse grained models for water journal, January 2019

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

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data journal, January 2019

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data journal, June 2019

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

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

A method for the solution of certain non-linear problems in least squares journal, January 1944

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 journal, March 2016

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

A molecular level explanation of the density maximum of liquid water from computer simulations with a polarizable potential model journal, February 2000

Physically informed artificial neural networks for atomistic modeling of materials journal, May 2019

A survey of transfer learning journal, May 2016

Construction of high-dimensional neural network potentials using environment-dependent atom pairs journal, May 2012

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections journal, April 2013

Atom-centered symmetry functions for constructing high-dimensional neural network potentials journal, February 2011

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

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

Machine learning coarse grained models for water
journal, January 2019

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

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
journal, January 2019

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
journal, June 2019

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

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

A method for the solution of certain non-linear problems in least squares
journal, January 1944

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
journal, March 2016

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

A molecular level explanation of the density maximum of liquid water from computer simulations with a polarizable potential model
journal, February 2000

Physically informed artificial neural networks for atomistic modeling of materials
journal, May 2019

A survey of transfer learning
journal, May 2016

Construction of high-dimensional neural network potentials using environment-dependent atom pairs
journal, May 2012

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
journal, April 2013

Atom-centered symmetry functions for constructing high-dimensional neural network potentials
journal, February 2011

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