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Title: Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

Abstract

The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials.more » Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3];  [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [4]
  1. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, United States
  2. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, United States; Department of Mechanical Engineering, University of Louisville, Louisville, Kentucky 40292, United States
  3. X-ray Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
  4. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, United States; Institute of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Scientific User Facilities Division
OSTI Identifier:
1542944
DOE Contract Number:  
AC02-06CH11357; AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Volume: 123; Journal Issue: 12; Journal ID: ISSN 1932-7447
Country of Publication:
United States
Language:
English

Citation Formats

Chan, Henry, Narayanan, Badri, Cherukara, Mathew J., Sen, Fatih G., Sasikumar, Kiran, Gray, Stephen K., Chan, Maria K. Y., and Sankaranarayanan, Subramanian K. R. S. Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data. United States: N. p., 2019. Web. doi:10.1021/acs.jpcc.8b09917.
Chan, Henry, Narayanan, Badri, Cherukara, Mathew J., Sen, Fatih G., Sasikumar, Kiran, Gray, Stephen K., Chan, Maria K. Y., & Sankaranarayanan, Subramanian K. R. S. Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data. United States. doi:10.1021/acs.jpcc.8b09917.
Chan, Henry, Narayanan, Badri, Cherukara, Mathew J., Sen, Fatih G., Sasikumar, Kiran, Gray, Stephen K., Chan, Maria K. Y., and Sankaranarayanan, Subramanian K. R. S. Thu . "Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data". United States. doi:10.1021/acs.jpcc.8b09917.
@article{osti_1542944,
title = {Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data},
author = {Chan, Henry and Narayanan, Badri and Cherukara, Mathew J. and Sen, Fatih G. and Sasikumar, Kiran and Gray, Stephen K. and Chan, Maria K. Y. and Sankaranarayanan, Subramanian K. R. S.},
abstractNote = {The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.},
doi = {10.1021/acs.jpcc.8b09917},
journal = {Journal of Physical Chemistry. C},
issn = {1932-7447},
number = 12,
volume = 123,
place = {United States},
year = {2019},
month = {1}
}