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Title: Multiobjective genetic training and uncertainty quantification of reactive force fields

Abstract

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO3 by H2S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.

Authors:
; ORCiD logo; ; ; ; ; ORCiD logo;
Publication Date:
Research Org.:
Univ. of Southern California, Los Angeles, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1461924
Alternate Identifier(s):
OSTI ID: 1501524
Grant/Contract Number:  
SC00014607; SC0014607
Resource Type:
Published Article
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Name: npj Computational Materials Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Mishra, Ankit, Hong, Sungwook, Rajak, Pankaj, Sheng, Chunyang, Nomura, Ken-ichi, Kalia, Rajiv K., Nakano, Aiichiro, and Vashishta, Priya. Multiobjective genetic training and uncertainty quantification of reactive force fields. United Kingdom: N. p., 2018. Web. doi:10.1038/s41524-018-0098-3.
Mishra, Ankit, Hong, Sungwook, Rajak, Pankaj, Sheng, Chunyang, Nomura, Ken-ichi, Kalia, Rajiv K., Nakano, Aiichiro, & Vashishta, Priya. Multiobjective genetic training and uncertainty quantification of reactive force fields. United Kingdom. doi:https://doi.org/10.1038/s41524-018-0098-3
Mishra, Ankit, Hong, Sungwook, Rajak, Pankaj, Sheng, Chunyang, Nomura, Ken-ichi, Kalia, Rajiv K., Nakano, Aiichiro, and Vashishta, Priya. Thu . "Multiobjective genetic training and uncertainty quantification of reactive force fields". United Kingdom. doi:https://doi.org/10.1038/s41524-018-0098-3.
@article{osti_1461924,
title = {Multiobjective genetic training and uncertainty quantification of reactive force fields},
author = {Mishra, Ankit and Hong, Sungwook and Rajak, Pankaj and Sheng, Chunyang and Nomura, Ken-ichi and Kalia, Rajiv K. and Nakano, Aiichiro and Vashishta, Priya},
abstractNote = {The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO3 by H2S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.},
doi = {10.1038/s41524-018-0098-3},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United Kingdom},
year = {2018},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1038/s41524-018-0098-3

Citation Metrics:
Cited by: 2 works
Citation information provided by
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Figures / Tables:

Fig. 1 Fig. 1: Snapshots for a initial and b final configurations during QMD simulation to study sulfidation of MoO3 flakes using H2S precursors. White, yellow, cyan, and red spheres represent H, S, Mo, and O atoms, respectively

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    Works referencing / citing this record:

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      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.