Journal
NPJ COMPUTATIONAL MATERIALS
Volume 6, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41524-020-00463-8
Keywords
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Funding
- Agence Nationale de Recherche, via the MEMOPAS project [ANR-19-CE46-0006-1]
- Euratom research and training program 2019-2020 [633053]
- National Nuclear Security Administration of the U.S. DOE [89233218CNA0000001]
- U.S. Department of Energy, Office of Nuclear Energy and Office of Science, Office of Advanced Scientific Computing Research through the Scientific Discovery through Advanced Computing (SciDAC) project on Fission Gas Behavior
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Defect diffusion is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant end-user intervention. These two bottlenecks prevent high-throughput implementations essential to propagate model-form uncertainty from interatomic interactions to predictive simulations. In order to address these issues, we extend a massively parallel accelerated sampling scheme, autonomously controlled by Bayesian estimators of statewide sampling completeness, to build atomistic kinetic Monte Carlo models on a state-space irreducible under exchange and space group symmetries. Focusing on isolated defects, we derive analytic expressions for drift and diffusion coefficients, providing a convergence metric by calculating the Kullback-Leibler divergence across the ensemble of diffusion processes consistent with the sampling uncertainty. The autonomy and efficacy of the method is demonstrated on surface trimers in tungsten and Hexa-interstitials in magnesium oxide, both of which exhibit complex, correlated migration mechanisms.
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