Computational design of energy‐related materials: From first‐principles calculations to machine learning

Abstract Energy‐related materials are crucial for advancing energy technologies, improving efficiency, reducing environmental impacts, and supporting sustainable development. Designing and discovering these materials through computational techniques necessitates a comprehensive understanding of the material space, which is defined by the constituent atoms, composition, and structure. Depending on the search space involved in the investigation, the computational materials design can be categorized into four primary approaches: atomic substitution in fixed prototype structures, crystal structure prediction (CSP), variable‐composition CSP, and inverse design across the entire materials space. This review provides an overview of these paradigms, detailing the concepts, strategies, and applications pertinent to energy‐related materials. The progression from first‐principles calculations to machine learning techniques is emphasized, with the aim of enhancing understanding and elucidating new advancements in computationally design of energy‐related materials. This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory