Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes.
4 affiliations
- 1Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki305-0044, Japan.
- 2Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan.
- 3Kyushu University Platform of Inter/Transdisciplinary Energy Research, Kyushu University , 744 Motooka, Nishi-ku, Fukuoka819-0395, Japan.
- 4Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba277-8561, Japan.
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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BibTeX
@article{koizumi2026kinetic,
title = {{Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes.}},
author = {Ai Koizumi and Tomofumi Tada and Ryo Tamura},
journal = {The journal of physical chemistry letters},
year = {2026},
month = sep,
doi = {10.1021/acs.jpclett.6c02157},
url = {https://europepmc.org/article/MED/42915475},
note = {CC BY},
}
RIS
TY - JOUR
TI - Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes.
AU - Ai Koizumi
AU - Tomofumi Tada
AU - Ryo Tamura
PY - 2026
DA - 2026/09/01
JO - The journal of physical chemistry letters
DO - 10.1021/acs.jpclett.6c02157
UR - https://europepmc.org/article/MED/42915475
AB - 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.
SN - 1948-7185
ER -