Atomistic modeling of meso-timescale processes with $\mathrm{SEAKMC}$: A perspective and recent developments

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 proce...

Full description

Saved in:
Bibliographic Details
Published in:Computational materials science 2021-04, Vol.194 (C)
Main Authors: Hayakawa, Sho, Isaacs, Jake, Medal, Hugh R., Xu, Haixuan
Format: Article
Language:English
Subjects:
machine learning
MATERIALS SCIENCE
meso-timescale modelling
on-the-fly kinetic Monte Carlo
saddle point search
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0927-0256
1879-0801