MDTS: automatic complex materials design using Monte Carlo tree search |
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| Authors: | Thaer M Dieb Shenghong Ju Kazuki Yoshizoe Zhufeng Hou Junichiro Shiomi |
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| Affiliation: | 1. National Institute for Materials Science, Tsukuba, Japan.;2. Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.;3. Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan.;4. RIKEN, Center for Advanced Intelligence Project, Tokyo, Japan.;5. Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan. |
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| Abstract: | AbstractComplex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library called MDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large Silicon-Germanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at https://github.com/tsudalab/MDTS. |
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| Keywords: | Materials informatics Materials design Monte Carlo tree search Si-Ge alloy interfacial structure Python library |
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