Machine learning for gas–solid interaction materials and devices

Abstract

Gas–solid interactions that involve various chemical and physical processes are important in various research fields such as gas storage and separation, catalysis, and gas sensing. The design and study of gas–interfacing materials and processes require thorough considerations on the structural, chemical and electrical properties at the gas–solid interfaces. However, due to the large number of materials available, conventional trial-and-error attempts take long development cycles and high costs. The recent advancement of machine learning (ML) for gas–solid interactions adequately addresses this challenge and provides new insights into materials design and property prediction, thus deserving a critical review and in-depth discussion. In this contribution, we reviewed the application of various ML methods in the development of materials and devices involving gas–solid interactions, focusing on the elaboration of the structure–property relationship established by ML models. We also discussed the potential opportunities and challenges for the future development in this field.