  Defect transport 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 statewise 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 defect transport tensors and
provide a convergence metric by calculating the Kullback-Leiber 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.
