[10a-Z09-1~9] 23.1 合同セッションN「インフォマティクス応用」

▲ [10a-Z09-6] Enhanced Monte Carlo Tree Search for Materials Design and Discovery

〇Sae Dieb¹、Masashi Ishii¹ (1.MaDIS, NIMS)

キーワード：Monte Carlo tree search

Materials design and discovery is often formulated as the selection of optimal solution from a space of candidates. Monte Carlo tree search (MCTS) has shown efficiency in solving this inverse design for several applications; however, randomization technique in MCTS can limit efficiency. In this work , we present an enhanced model of MCTS with policy gradient, a reinforcement learning algorithm.