Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors
The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining environments. However, the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screeni...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Mining Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095268624001770 |
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| _version_ | 1850057762857287680 |
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| author | Ran Zhang Guo Chen Shasha Gao Lu Chen Yongchao Cheng Xiuquan Gu Yue Wang |
| author_facet | Ran Zhang Guo Chen Shasha Gao Lu Chen Yongchao Cheng Xiuquan Gu Yue Wang |
| author_sort | Ran Zhang |
| collection | DOAJ |
| description | The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining environments. However, the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screening of gas sensitive materials. Consequently, this paper introduced a novel screening approach that integrates first principles with machine learning (ML) to rapidly predict the gas sensitivity of materials. Initially, a comprehensive database of multi-physical parameters was established by modeling various adsorption sites on the surface of WO3, which serves as a representative material. Since density functional theory (DFT) is one of the first principles, DFT calculations were conducted to derive essential multi-physical parameters, including bandgap, density of states (DOS), Fermi level, adsorption energy, and structural modifications resulting from adsorption. The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. The model’s performance was assessed through receiver operating characteristic (ROC) curves, confusion matrices, and other evaluation metrics, ultimately achieving a prediction accuracy of 90% for identifying key features influencing gas adsorption performance. This proposed strategy for predicting the gas sensitive characteristics of materials holds significant potential for application in identifying additional gas sensitive properties across various materials. |
| format | Article |
| id | doaj-art-d6cf197be4c24dc5bda1ae7dc9baf401 |
| institution | DOAJ |
| issn | 2095-2686 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Mining Science and Technology |
| spelling | doaj-art-d6cf197be4c24dc5bda1ae7dc9baf4012025-08-20T02:51:19ZengElsevierInternational Journal of Mining Science and Technology2095-26862024-12-0134121765177210.1016/j.ijmst.2024.12.001Combining first principles and machine learning for rapid assessment response of WO3 based gas sensorsRan Zhang0Guo Chen1Shasha Gao2Lu Chen3Yongchao Cheng4Xiuquan Gu5Yue Wang6School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, ChinaSchool of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; Corresponding author.The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining environments. However, the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screening of gas sensitive materials. Consequently, this paper introduced a novel screening approach that integrates first principles with machine learning (ML) to rapidly predict the gas sensitivity of materials. Initially, a comprehensive database of multi-physical parameters was established by modeling various adsorption sites on the surface of WO3, which serves as a representative material. Since density functional theory (DFT) is one of the first principles, DFT calculations were conducted to derive essential multi-physical parameters, including bandgap, density of states (DOS), Fermi level, adsorption energy, and structural modifications resulting from adsorption. The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. The model’s performance was assessed through receiver operating characteristic (ROC) curves, confusion matrices, and other evaluation metrics, ultimately achieving a prediction accuracy of 90% for identifying key features influencing gas adsorption performance. This proposed strategy for predicting the gas sensitive characteristics of materials holds significant potential for application in identifying additional gas sensitive properties across various materials.http://www.sciencedirect.com/science/article/pii/S2095268624001770Machine learningDensity functional theoryRapid assessmentGas sensor |
| spellingShingle | Ran Zhang Guo Chen Shasha Gao Lu Chen Yongchao Cheng Xiuquan Gu Yue Wang Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors International Journal of Mining Science and Technology Machine learning Density functional theory Rapid assessment Gas sensor |
| title | Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors |
| title_full | Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors |
| title_fullStr | Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors |
| title_full_unstemmed | Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors |
| title_short | Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors |
| title_sort | combining first principles and machine learning for rapid assessment response of wo3 based gas sensors |
| topic | Machine learning Density functional theory Rapid assessment Gas sensor |
| url | http://www.sciencedirect.com/science/article/pii/S2095268624001770 |
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