Machine learning for inverse problems in materials research

Abstract

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.