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Title: Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

Here, a massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.
Authors:
ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-18-21562
Journal ID: ISSN 2475-9953; PRMHAR
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 2; Journal Issue: 5; Journal ID: ISSN 2475-9953
Publisher:
American Physical Society (APS)
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Material Science
OSTI Identifier:
1458961

Swinburne, Thomas David, and Perez, Danny. Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification. United States: N. p., Web. doi:10.1103/PhysRevMaterials.2.053802.
Swinburne, Thomas David, & Perez, Danny. Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification. United States. doi:10.1103/PhysRevMaterials.2.053802.
Swinburne, Thomas David, and Perez, Danny. 2018. "Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification". United States. doi:10.1103/PhysRevMaterials.2.053802.
@article{osti_1458961,
title = {Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification},
author = {Swinburne, Thomas David and Perez, Danny},
abstractNote = {Here, a massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.},
doi = {10.1103/PhysRevMaterials.2.053802},
journal = {Physical Review Materials},
number = 5,
volume = 2,
place = {United States},
year = {2018},
month = {5}
}