{"title":"用于气体传感器的 Mo2C 中过渡金属的密度泛函理论与机器学习","authors":"Weiguang Huang, Zhongzhou Dong* and Long Lin, ","doi":"10.1021/acsanm.4c0427410.1021/acsanm.4c04274","DOIUrl":null,"url":null,"abstract":"<p >Gas accumulation is the primary cause of explosions in underground mines, and preventing it requires effective gas detection. To address this, we propose an approach combining machine learning (ML) and density functional theory (DFT) for designing nanoscale gas sensors. Our study demonstrates that a back-propagation neural network (BPNN) model, optimized with suitable hyperparameters, achieves high accuracy with an R<sup>2</sup> (coefficient of determination) of 0.92 and a low RMSE (root-mean-square error) of 0.24 in predicting the substrate material formed by transition metal (TM)-doped Mo<sub>2</sub>C and its interaction with key gas molecules (CO, H<sub>2</sub>S, CH<sub>4</sub>, and C<sub>2</sub>H<sub>6</sub>). Based on these interaction strengths, we have analyzed the materials in more depth. Additionally, we find that certain features directly affect the increase or decrease of interaction strengths within a specific range, providing insights that contribute to the design of more efficient nanoscale sensors.</p>","PeriodicalId":6,"journal":{"name":"ACS Applied Nano Materials","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Density Functional Theory and Machine Learning of Transition Metals in Mo2C for Gas Sensors\",\"authors\":\"Weiguang Huang, Zhongzhou Dong* and Long Lin, \",\"doi\":\"10.1021/acsanm.4c0427410.1021/acsanm.4c04274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Gas accumulation is the primary cause of explosions in underground mines, and preventing it requires effective gas detection. To address this, we propose an approach combining machine learning (ML) and density functional theory (DFT) for designing nanoscale gas sensors. Our study demonstrates that a back-propagation neural network (BPNN) model, optimized with suitable hyperparameters, achieves high accuracy with an R<sup>2</sup> (coefficient of determination) of 0.92 and a low RMSE (root-mean-square error) of 0.24 in predicting the substrate material formed by transition metal (TM)-doped Mo<sub>2</sub>C and its interaction with key gas molecules (CO, H<sub>2</sub>S, CH<sub>4</sub>, and C<sub>2</sub>H<sub>6</sub>). Based on these interaction strengths, we have analyzed the materials in more depth. Additionally, we find that certain features directly affect the increase or decrease of interaction strengths within a specific range, providing insights that contribute to the design of more efficient nanoscale sensors.</p>\",\"PeriodicalId\":6,\"journal\":{\"name\":\"ACS Applied Nano Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Nano Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsanm.4c04274\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Nano Materials","FirstCategoryId":"88","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsanm.4c04274","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Density Functional Theory and Machine Learning of Transition Metals in Mo2C for Gas Sensors
Gas accumulation is the primary cause of explosions in underground mines, and preventing it requires effective gas detection. To address this, we propose an approach combining machine learning (ML) and density functional theory (DFT) for designing nanoscale gas sensors. Our study demonstrates that a back-propagation neural network (BPNN) model, optimized with suitable hyperparameters, achieves high accuracy with an R2 (coefficient of determination) of 0.92 and a low RMSE (root-mean-square error) of 0.24 in predicting the substrate material formed by transition metal (TM)-doped Mo2C and its interaction with key gas molecules (CO, H2S, CH4, and C2H6). Based on these interaction strengths, we have analyzed the materials in more depth. Additionally, we find that certain features directly affect the increase or decrease of interaction strengths within a specific range, providing insights that contribute to the design of more efficient nanoscale sensors.
期刊介绍:
ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.