Boosting material modeling using game tree search

We demonstrate a heuristic optimization algorithm based on the game tree search for multicomponent materials design. The algorithm searches for the largest spin polarization of seven-component Heusler alloys. The algorithm can find the peaks quickly and is more robust against local optima than Bayesian optimization approaches using the expected improvement or upper confidence bound approaches. We also investigate Heusler alloys, including antisite disorder, and we show that [Fe0.9Co0.1]₂Cr0.95Mn0.05Si0.3Ge0.7 has the potential to be a high-spin-polarized material with robustness against antisite disorder.