M Dieb Thaer, Ju Shenghong, Yoshizoe Kazuki, Hou Zhufeng, Shiomi Junichiro, Tsuda Koji
National Institute for Materials Science, Tsukuba, Japan.
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
Sci Technol Adv Mater. 2017 Jul 20;18(1):498-503. doi: 10.1080/14686996.2017.1344083. eCollection 2017.
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.
复杂材料设计通常被表示为一个黑箱组合优化问题。在本文中，我们展示了一个名为MDTS（使用树搜索的材料设计）的新型Python库。我们的算法采用蒙特卡洛树搜索方法，该方法在计算机围棋游戏中已展现出卓越性能。与需要用户干预以适当设置参数的进化算法不同，MDTS没有调优参数，并且能在各种问题中自主运行。与贝叶斯优化软件包相比，我们的算法显示出具有竞争力的搜索效率和卓越的可扩展性。我们成功设计出了大型硅锗（Si-Ge）合金结构，而贝叶斯优化由于计算成本过高无法处理此类结构。MDTS可在https://github.com/tsudalab/MDTS获取。
