MDTS: automatic complex materials design using Monte Carlo tree search

Thaer M. Dieb, Shenghong Ju, Kazuki Yoshizoe, Zhufeng Hou, Junichiro Shiomi, Koji Tsuda

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)498-503
Number of pages6
JournalScience and Technology of Advanced Materials
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 1 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Materials Science(all)

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