Machine learning for inverse problems in materials research

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  • YOSHIDA Ryo
    The Institute of Statistical Mathematics, Research Organization of Information and Systems National Institute for Materials Science (NIMS) School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, SOKENDAI
  • IWAYAMA Megumi
    School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, SOKENDAI Daicel Corporation
  • GUO Zhongliang
    The Institute of Statistical Mathematics, Research Organization of Information and Systems

Bibliographic Information

Other Title
  • 材料研究の逆問題と機械学習
  • ザイリョウ ケンキュウ ノ ギャクモンダイ ト キカイ ガクシュウ

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Abstract

<p>We illustrate applications of machine learning technologies to several inverse problems in materials research. The objective of the forward problem is to predict the output of a system with respect to its input. For example, the input variable corresponds to the structure of a given material and the output variable corresponds to its properties. In the inverse problem, we identify promising candidate materials that exhibit any given set of desired properties by solving the inverse mapping of the forward model. This is a conventional workflow of data science, but one distinct feature of data analysis in materials research lies in the high dimensionality and specificity of the variables. In general, the search space for candidate materials is extremely vast. In addition, in many cases, we deal with variables that are non-trivial to be represented into fixed-length vectors, such as composition, molecules, and crystal structures. In this paper, we describe the essence of machine learning for solving inverse problems by introducing various examples.</p>

Journal

  • Oyo Buturi

    Oyo Buturi 90 (7), 428-432, 2021-07-05

    The Japan Society of Applied Physics

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