Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
Abstract
Here, we propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of largescale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. Conversely, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new datadriven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple countbased reduction. For our liquid methane system,more »
 Authors:

 Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering
 Pontifical Catholic Univ. of Chile, Santiago (Chile). Mathematical and Computational Engineering
 Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering and Dept. of Materials Science and Engineering
 Publication Date:
 Research Org.:
 Washington State Univ., Pullman, WA (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1367888
 Grant/Contract Number:
 NA0002007
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Chemical Science
 Additional Journal Information:
 Journal Volume: 679; Journal ID: ISSN 20416520
 Publisher:
 Royal Society of Chemistry
 Country of Publication:
 United States
 Language:
 English
 Subject:
 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Yang, Qian, SingLong, Carlos A., and Reed, Evan J. Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics. United States: N. p., 2017.
Web. doi:10.1039/c7sc01052d.
Yang, Qian, SingLong, Carlos A., & Reed, Evan J. Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics. United States. doi:10.1039/c7sc01052d.
Yang, Qian, SingLong, Carlos A., and Reed, Evan J. Mon .
"Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics". United States. doi:10.1039/c7sc01052d. https://www.osti.gov/servlets/purl/1367888.
@article{osti_1367888,
title = {Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics},
author = {Yang, Qian and SingLong, Carlos A. and Reed, Evan J.},
abstractNote = {Here, we propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of largescale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. Conversely, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new datadriven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple countbased reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. Furthermore, we describe a framework in this work that paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.},
doi = {10.1039/c7sc01052d},
journal = {Chemical Science},
number = ,
volume = 679,
place = {United States},
year = {2017},
month = {6}
}
Web of Science
Works referenced in this record:
Neural Networks for the Prediction of Organic Chemistry Reactions
journal, October 2016
 Wei, Jennifer N.; Duvenaud, David; AspuruGuzik, Alán
 ACS Central Science, Vol. 2, Issue 10
Exact stochastic simulation of coupled chemical reactions
journal, December 1977
 Gillespie, Daniel T.
 The Journal of Physical Chemistry, Vol. 81, Issue 25
Optimallyreduced kinetic models: reaction elimination in largescale kinetic mechanisms
journal, November 2003
 Bhattacharjee, Binita; Schwer, Douglas A.; Barton, Paul I.
 Combustion and Flame, Vol. 135, Issue 3
Carbon Cluster Formation during Thermal Decomposition of Octahydro1,3,5,7tetranitro1,3,5,7tetrazocine and 1,3,5Triamino2,4,6trinitrobenzene High Explosives from ReaxFF Reactive Molecular Dynamics Simulations
journal, October 2009
 Zhang, Luzheng; Zybin, Sergey V.; van Duin, Adri C. T.
 The Journal of Physical Chemistry A, Vol. 113, Issue 40
First principles simulation of a superionic phase of hydrogen fluoride (HF) at high pressures and temperatures
journal, July 2006
 Goldman, Nir; Fried, Laurence E.
 The Journal of Chemical Physics, Vol. 125, Issue 4
Modeling and Simulating Chemical Reactions
journal, January 2008
 Higham, Desmond J.
 SIAM Review, Vol. 50, Issue 2
Examination of the concept of degree of rate control by firstprinciples kinetic Monte Carlo simulations
journal, June 2009
 Meskine, Hakim; Matera, Sebastian; Scheffler, Matthias
 Surface Science, Vol. 603, Issue 1012
A general method for numerically simulating the stochastic time evolution of coupled chemical reactions
journal, December 1976
 Gillespie, Daniel T.
 Journal of Computational Physics, Vol. 22, Issue 4
Discovering chemistry with an ab initio nanoreactor
journal, November 2014
 Wang, LeePing; Titov, Alexey; McGibbon, Robert
 Nature Chemistry, Vol. 6, Issue 12
Approximate accelerated stochastic simulation of chemically reacting systems
journal, July 2001
 Gillespie, Daniel T.
 The Journal of Chemical Physics, Vol. 115, Issue 4
Stability of hydrocarbons at deep Earth pressures and temperatures
journal, April 2011
 Spanu, L.; Donadio, D.; Hohl, D.
 Proceedings of the National Academy of Sciences, Vol. 108, Issue 17
Fast Parallel Algorithms for ShortRange Molecular Dynamics
journal, March 1995
 Plimpton, Steve
 Journal of Computational Physics, Vol. 117, Issue 1
Firstprinciples and classical molecular dynamics simulation of shocked polymers
journal, February 2010
 Mattsson, Thomas R.; Lane, J. Matthew D.; Cochrane, Kyle R.
 Physical Review B, Vol. 81, Issue 5
Simulations of Shocked Methane Including SelfConsistent Semiclassical Quantum Nuclear Effects
journal, October 2012
 Qi, Tingting; Reed, Evan J.
 The Journal of Physical Chemistry A, Vol. 116, Issue 42