{"title":"通过密度泛函理论和机器学习快速发现材料中的气体反应","authors":"Shasha Gao, Yongchao Cheng, Lu Chen, Sheng Huang","doi":"10.1002/eem2.12816","DOIUrl":null,"url":null,"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 Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> 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 Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> to hydrogen sulfide, the resulting model predicts the gas response of H<sub>2</sub>S; 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.","PeriodicalId":11554,"journal":{"name":"Energy & Environmental Materials","volume":null,"pages":null},"PeriodicalIF":13.0000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning\",\"authors\":\"Shasha Gao, Yongchao Cheng, Lu Chen, Sheng Huang\",\"doi\":\"10.1002/eem2.12816\",\"DOIUrl\":null,\"url\":null,\"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 Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> 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 Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> to hydrogen sulfide, the resulting model predicts the gas response of H<sub>2</sub>S; 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.\",\"PeriodicalId\":11554,\"journal\":{\"name\":\"Energy & Environmental Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy & Environmental Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1002/eem2.12816\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy & Environmental Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/eem2.12816","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning
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.
期刊介绍:
Energy & Environmental Materials (EEM) is an international journal published by Zhengzhou University in collaboration with John Wiley & Sons, Inc. The journal aims to publish high quality research related to materials for energy harvesting, conversion, storage, and transport, as well as for creating a cleaner environment. EEM welcomes research work of significant general interest that has a high impact on society-relevant technological advances. The scope of the journal is intentionally broad, recognizing the complexity of issues and challenges related to energy and environmental materials. Therefore, interdisciplinary work across basic science and engineering disciplines is particularly encouraged. The areas covered by the journal include, but are not limited to, materials and composites for photovoltaics and photoelectrochemistry, bioprocessing, batteries, fuel cells, supercapacitors, clean air, and devices with multifunctionality. The readership of the journal includes chemical, physical, biological, materials, and environmental scientists and engineers from academia, industry, and policy-making.