Multi-objective optimization and its application in materials science

Abstract

Optimizing more than one property is inevitable in designing new materials; however, some properties are usually improved at the expense of others. Multi-objective optimization methods in engineering and computer science have proven to be an effective means to optimize several different properties simultaneously. Here, we reviewed these approaches including scalarization, evolutionary algorithms, and especially Bayesian optimization. Their promising applications to a number of materials problems are also discussed in the paper.