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Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning
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  • Shasha Gao,
  • Yongchao Cheng,
  • Lu Chen,
  • Sheng Huang
Shasha Gao
China University of Mining and Technology
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Yongchao Cheng
China University of Mining and Technology
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Lu Chen
China University of Mining and Technology
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Sheng Huang
University of Macau

Corresponding Author:shenghuang@um.edu.mo

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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.
Submitted to Energy & Environmental Materials
11 May 2024Assigned to Editor
11 May 2024Submission Checks Completed
15 May 2024Reviewer(s) Assigned
12 Jun 20241st Revision Received
21 Jun 2024Review(s) Completed, Editorial Evaluation Pending
21 Jun 2024Reviewer(s) Assigned
04 Jul 2024Editorial Decision: Accept