Atomistic modeling of meso-timescale processes with $$\mathrm{SEAKMC}$$: A perspective and recent developments
Abstract
On-the-fly kinetic Monte Carlo (kMC) methods have recently garnered significant attentions after successful applications to various atomic-scale problems using a timescale outside the reach of classical molecular dynamics. These methods play a critical role in modeling atomistic meso-timescale processes, and it is therefore essential to further improve their capabilities. In this report we review one of the on-the-fly kMC methods, Self-Evolving Atomistic kinetic Monte Carlo (SEAKMC) and propose two schemes that considerably enhance the efficiency of saddle point searches (SPSs) during the simulations. The performance of these schemes is tested using the diffusion of point defects in bcc Fe. In addition, we discuss approaches to significantly mitigate limitations of these schemes, which further improves their efficiencies. Importantly, these schemes improve the SPS efficiency not only for SEAKMC but also for other on-the-fly kMC methods, broadening the applications of on-the-fly kMC simulations to complex meso-timescale problems.
- Publication Date:
- Research Org.:
- Univ. of Tennessee, Knoxville, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); University of Tennessee
- OSTI Identifier:
- 1852889
- Alternate Identifier(s):
- OSTI ID: 1818542
- Grant/Contract Number:
- SC0019151; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computational Materials Science
- Additional Journal Information:
- Journal Volume: 194; Journal Issue: C; Journal ID: ISSN 0927-0256
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; meso-timescale modelling; saddle point search; machine learning; on-the-fly kinetic Monte Carlo
Citation Formats
Hayakawa, Sho, Isaacs, Jake, Medal, Hugh R., and Xu, Haixuan. Atomistic modeling of meso-timescale processes with $\mathrm{SEAKMC}$: A perspective and recent developments. United States: N. p., 2021.
Web. doi:10.1016/j.commatsci.2021.110390.
Hayakawa, Sho, Isaacs, Jake, Medal, Hugh R., & Xu, Haixuan. Atomistic modeling of meso-timescale processes with $\mathrm{SEAKMC}$: A perspective and recent developments. United States. https://doi.org/10.1016/j.commatsci.2021.110390
Hayakawa, Sho, Isaacs, Jake, Medal, Hugh R., and Xu, Haixuan. Fri .
"Atomistic modeling of meso-timescale processes with $\mathrm{SEAKMC}$: A perspective and recent developments". United States. https://doi.org/10.1016/j.commatsci.2021.110390. https://www.osti.gov/servlets/purl/1852889.
@article{osti_1852889,
title = {Atomistic modeling of meso-timescale processes with $\mathrm{SEAKMC}$: A perspective and recent developments},
author = {Hayakawa, Sho and Isaacs, Jake and Medal, Hugh R. and Xu, Haixuan},
abstractNote = {On-the-fly kinetic Monte Carlo (kMC) methods have recently garnered significant attentions after successful applications to various atomic-scale problems using a timescale outside the reach of classical molecular dynamics. These methods play a critical role in modeling atomistic meso-timescale processes, and it is therefore essential to further improve their capabilities. In this report we review one of the on-the-fly kMC methods, Self-Evolving Atomistic kinetic Monte Carlo (SEAKMC) and propose two schemes that considerably enhance the efficiency of saddle point searches (SPSs) during the simulations. The performance of these schemes is tested using the diffusion of point defects in bcc Fe. In addition, we discuss approaches to significantly mitigate limitations of these schemes, which further improves their efficiencies. Importantly, these schemes improve the SPS efficiency not only for SEAKMC but also for other on-the-fly kMC methods, broadening the applications of on-the-fly kMC simulations to complex meso-timescale problems.},
doi = {10.1016/j.commatsci.2021.110390},
journal = {Computational Materials Science},
number = C,
volume = 194,
place = {United States},
year = {Fri Apr 09 00:00:00 EDT 2021},
month = {Fri Apr 09 00:00:00 EDT 2021}
}
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