Kinetic Monte Carlo–Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes

Abstract

Abstract Maximizing ionic conductivity remains a fundamental challenge in the atomistic design of solid electrolytes. To this end, we present a framework that combines kinetic Monte Carlo (KMC) and factorization machine with quadratic-optimization annealing (FMQA), an Ising-machine-based black-box optimization algorithm for large-scale combinatorial problems. KMC evaluates the ionic conductivity for a given dopant configuration, whereas FMQA iteratively learns a surrogate model from a small configuration-conductivity data set and proposes configurations expected to maximize conductivity. To address the severe combinatorial explosion in large KMC simulation cells, we partition the configuration space for parallel optimization. As a proof of concept, we apply this KMC–FMQA framework to bulk 8 mol % yttria-stabilized zirconia, identifying a dopant configuration with an order-of-magnitude higher conductivity than that of random configurations. Combined with experiments, this framework will enable the determination of microscopic structures from measured conductivity, providing insight into the underlying transport mechanisms.