Metaheuristic-based inverse design of materials – A survey

There is a growing interest in the inverse approach to material deign, in which the desired target properties are used as input to identify the atomic identity, composition and structure (ACS) that exhibit such properties. As an overview, this paper surveys and summarizes previous works in metaheuristic-based inverse design of various materials. The basics of metaheuristic-based inverse design of materials are presented, including feature identification (fingerprinting), machine learning of ACS→property models (forward design), metaheuristic algorithms for property→ACS predictions (inverse design), and experimental validations, with focus on inverse design. The past studies are organized into a two-level hierarchy with how properties are predicted at the higher level, either by first principles, simulation, or machine learning model, and the number of target properties considered at the lower level, either one or more than one. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. This review intends to serve as the steppingstone/springboard for those interested in advancing this area of research.