Computational discovery of energy materials in the era of big data and machine learning: A critical review

Abstract

The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies. Despite the rapid development of computational infrastructures and theoretical approaches, progress so far has been limited by the empirical and serial nature of experimental work. Fortunately, the situation is changing thanks to the maturation of theoretical tools such as density functional theory, high-throughput screening, crystal structure prediction, and emerging approaches based on machine learning. Together these recent innovations in computational chemistry, data informatics, and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries. In this report, recent advances in material discovery methods are reviewed for energy devices. Three paradigms based on empiricism-driven experiments, database-driven high-throughput screening, and data informatics-driven machine learning are discussed critically. Key methodological advancements involved are reviewed including high-throughput screening, crystal structure prediction, and generative models for target material design. Their applications in energy-related devices such as batteries, catalysts, and photovoltaics are selectively showcased.