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

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.

arXiv comment14 pages, 7 figures

References in corpus (4)

• Big Data of Materials Science - Critical Role of the Descriptor
• Kinetic Activation Relaxation Technique
• Monte Carlo with Absorbing Markov Chains: Fast Local Algorithms for Slow Dynamics
• The nature of high-energy radiation damage in iron: Modeling results

Cited by in corpus (7)

• Parameter-free quantitative simulation of high dose microstructure and hydrogen retention in ion-irradiated tungsten
• Atomistic-Object Kinetic Monte Carlo simulations of irradiation damage in tungsten
• A Semi-Grand Canonical Kinetic Monte Carlo study of Single-Walled Carbon Nanotubes growth
• Interstitialcy-based reordering kinetics of Ni${}_3$Al precipitates in irradiated Ni-based super alloys
• Compressing and forecasting atomic material simulations with descriptors
• Reaction-drift-diffusion models from master equations: application to material defects
• Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs