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Monte Carlo tree search for materials design and discovery

  • Thaer M. Dieb (a1) (a2) (a3), Shenghong Ju (a1) (a4), Junichiro Shiomi (a1) (a4) (a5) and Koji Tsuda (a1) (a2) (a3)

Abstract

Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. Since the number of candidates can be exponentially proportional to the structure determination variables, the optimal structure must be obtained efficiently. Recently, inspired by its success in the Go computer game, several approaches have applied Monte Carlo tree search (MCTS) to solve optimization problems in natural sciences including materials science. In this paper, we briefly reviewed applications of MCTS in materials design and discovery, and analyzed its future potential.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Address all correspondence to Koji Tsuda at tsuda@k.u-tokyo.ac.jp

References

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Monte Carlo tree search for materials design and discovery

  • Thaer M. Dieb (a1) (a2) (a3), Shenghong Ju (a1) (a4), Junichiro Shiomi (a1) (a4) (a5) and Koji Tsuda (a1) (a2) (a3)

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