Machine learning: Accelerating materials development for energy storage and conversion

TLDR

Energy demand is growing worldwide, but traditional materials discovery is slow and costly, prompting interest in machine learning as a transformative approach. This review aims to outline the basic procedures and common algorithms of machine learning in materials science and highlight recent advances in property prediction and materials development for energy-related applications such as catalysis, batteries, solar cells, and gas capture. The authors describe how machine learning models are applied to predict material properties and accelerate development, and discuss how ML contributes to experimental design and execution. The review anticipates that machine learning will drive future progress in materials science for energy technologies. An image is included.

Abstract

Abstract With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery. In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energy‐related fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. image