Multi-objective optimization in machine learning assisted materials design and discovery

Abstract

Over the past decades, machine learning has kept playing an important role in materials design and discovery. In practical applications, materials usually need to fulfill the requirements of multiple target properties. Therefore, multi-objective optimization of materials based on machine learning has become one of the most promising directions. This review aims to provide a detailed discussion on machine learning-assisted multi-objective optimization in materials design and discovery combined with the recent research progress. First, we briefly introduce the workflow of materials machine learning. Then, the Pareto fronts in multi-objective optimization and the corresponding algorithms are summarized. Next, multi-objective optimization strategies are demonstrated, including Pareto front-based strategy, scalarization function, and constraint method. Subsequently, the research progress of multi-objective optimization in materials machine learning is summarized and different Pareto front-based strategies are discussed. Finally, we propose future directions for machine learning-based multi-objective optimization of materials.