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説明:

Complex 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.

権利情報:

キーワード: Monte Carlo tree search, materials design

刊行年月日: 2017-12-31

出版者: Taylor & Francis

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研究助成金:

原稿種別: 査読前原稿 (Author's original)

MDR DOI:

公開URL: https://doi.org/10.1080/14686996.2017.1344083

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更新時刻: 2024-01-05 22:12:03 +0900

MDRでの公開時刻: 2021-08-14 03:54:46 +0900