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 multi-source 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, which 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.