Atomistic modeling of meso-timescale processes with SEAKMC: A perspective and recent developments

S Hayakawa and J Isaacs and HR Medal and HX Xu, COMPUTATIONAL MATERIALS SCIENCE, 194, 110390 (2021).

DOI: 10.1016/j.commatsci.2021.110390

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. Herein, we review one of the on-the- fly kMC methods, SelfEvolving 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- thefly kMC simulations to complex meso-timescale problems.

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