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

Ai Koizumi; Tomofumi Tada; Ryo Tamura

doi:10.1021/acs.jpclett.6c02157

ジャーナル論文 Kinetic Monte Carlo–Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes

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Ai Koizumi, Tomofumi Tada, Ryo Tamura. Kinetic Monte Carlo–Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes. The Journal of Physical Chemistry Letters. 2026, 17 (37), 10758-10762. https://doi.org/10.1021/acs.jpclett.6c02157

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(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.

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