Automated calculation and convergence of defect transport tensors

T D Swinburne (1, 2) , Danny Perez (3)

1 CNRS - Centre National de la Recherche Scientifique
2 CINaM - Centre Interdisciplinaire de Nanoscience de Marseille
3 LANL - Los Alamos National Laboratory

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.

Domaines: Science des matériaux [cond-mat.mtrl-sci]

T D Swinburne, Danny Perez. Automated calculation and convergence of defect transport tensors. npj Computational Materials, 2020, ⟨10.1038/s41524-020-00463-8⟩. ⟨hal-03053924⟩