2 papers
cond-mat.mtrl-sci2026
Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes
Ai Koizumi, Tomofumi Tada, Ryo Tamura
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 (…
cs.CY2025
Exploring utilization of generative AI for research and education in data-driven materials science
Takahiro Misawa, Ai Koizumi, Ryo Tamura +1
Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials sci…