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Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species an...

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Autores principales: Yang, Qian, Sing-Long, Carlos A., Reed, Evan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5625287/
https://www.ncbi.nlm.nih.gov/pubmed/28989618
http://dx.doi.org/10.1039/c7sc01052d
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author Yang, Qian
Sing-Long, Carlos A.
Reed, Evan J.
author_facet Yang, Qian
Sing-Long, Carlos A.
Reed, Evan J.
author_sort Yang, Qian
collection PubMed
description We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale 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. In contrast, 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 data-driven 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 count-based 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. The framework described in this work 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.
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spelling pubmed-56252872017-10-06 Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics Yang, Qian Sing-Long, Carlos A. Reed, Evan J. Chem Sci Chemistry We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale 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. In contrast, 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 data-driven 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 count-based 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. The framework described in this work 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. Royal Society of Chemistry 2017-08-01 2017-06-19 /pmc/articles/PMC5625287/ /pubmed/28989618 http://dx.doi.org/10.1039/c7sc01052d Text en This journal is © The Royal Society of Chemistry 2017 http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Chemistry
Yang, Qian
Sing-Long, Carlos A.
Reed, Evan J.
Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title_full Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title_fullStr Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title_full_unstemmed Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title_short Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
title_sort learning reduced kinetic monte carlo models of complex chemistry from molecular dynamics
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5625287/
https://www.ncbi.nlm.nih.gov/pubmed/28989618
http://dx.doi.org/10.1039/c7sc01052d
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