Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning

Abstract

In this study, a framework for predicting the gas-sensitive properties of gas-sensitive materials by combining machine learning and density functional theory (DFT) has been proposed. The framework rapidly predicts the gas response of materials by establishing relationships between multisource physical parameters and gas-sensitive properties. In order to prove its effectiveness, the perovskite Cs₃Cu₂I₅ has been selected as the representative material. The physical parameters before and after the adsorption of various gases have been calculated using DFT, and then a machine learning model has been trained based on these parameters. Previous studies have shown that a single physical parameter alone is not enough to accurately predict the gas sensitivity of materials. Therefore, a variety of physical parameters have been selected for machine learning, and the final machine learning model achieved 92% accuracy in predicting gas sensitivity. It is important to note that although there have been no previous reports on the response of Cs₃Cu₂I₅ to hydrogen sulfide, the resulting model predicts the gas response of H₂S; it is subsequently confirmed experimentally. This method not only enhances the understanding of the gas sensing mechanism, but also has a universal nature, making it suitable for the development of various new gas-sensitive materials.