Monte Carlo tree search for materials design and discovery
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rtificial Intelligence Prospective
Monte Carlo tree search for materials design and discovery Thaer M. Dieb, National Institute for Materials Science, Tsukuba, Japan; Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan; RIKEN, AIP, Tokyo, Japan Shenghong Ju, National Institute for Materials Science, Tsukuba, Japan; Department of Mechanical Engineering, the University of Tokyo, Tokyo, Japan Junichiro Shiomi, National Institute for Materials Science, Tsukuba, Japan; Department of Mechanical Engineering, the University of Tokyo, Tokyo, Japan; CREST, JST, Tokyo, Japan Koji Tsuda, National Institute for Materials Science, Tsukuba, Japan; Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan; RIKEN, AIP, Tokyo, Japan Address all correspondence to Koji Tsuda at [email protected] (Received 15 January 2019; accepted 18 March 2019)
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.
Introduction The ability to design a material with desired properties a priori using computational methods has been promised by computational materials science for many years.[1] This problem can be framed as selecting the optimal composite material structure that meets certain quality metrics from a space of candidates.[2,3] One example is the structural determination of a substitutional alloy problem in solid-state materials design,[4,5] where optimal atoms (or vacancies) assignment in a crystal structure is determined to maximize or minimize a target property. To accelerate this process, researchers have emphasized data-driven and machine learning approaches as the fourth paradigm of science.[6,7] Data-driven materials design approaches are iterative design algorithms. Given a space of candidates S, the algorithm aims to find the optimal candidate pbst that optimizes a black-box function f ( p) (usually the target property). Starting with a random selection, and within a predefined number of iterations, the algorithm evaluates a set of selected candidates and obtains feedback for a more informed selection on the next iteration. The function f ( p) is evaluated by experiment or simulation and it is computationally expensive to query. It is necessary to reach the optimal candidate with as few queries as possible[8] (Fig. 1). Several methods have been applied for data-driven materials design. Evolutionary algorithms, such as genetic algorithms[9,10] that use human evolution mechanisms (such as crossover and mutation),
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