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

TL;DR

This paper introduces a Bayesian-based, autonomous method for constructing transition rate matrices from accelerated atomistic simulations, enabling accurate long-timescale modeling of complex systems with quantified uncertainties.

Contribution

It presents a novel, self-optimizing approach that combines Bayesian Markov models with temperature acceleration to efficiently explore and quantify uncertainties in transition rate matrices.

Findings

Method accurately models defect dynamics over seconds.

Uncertainty quantification improves simulation reliability.

Autonomous exploration optimizes sampling efficiency.

Abstract

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 ac- celeration 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.