{"title":"基于DFT和机器学习的单原子合金催化剂的设计与筛选","authors":"Wenyu Zhou, Haisong Feng, Shihong Zhou, Mengxin Wang, Yuping Chen, Chenyang Lu, Hao Yuan, Jing Yang, Qun Li, Luxi Tan, Lichun Dong, Yong-Wei Zhang","doi":"10.1002/aic.18678","DOIUrl":null,"url":null,"abstract":"Carbon dioxide (CO<sub>2</sub>) utilization technology is of great significance for achieving carbon neutrality, in which the catalytic materials play crucial roles, and among them, single-atom alloys (SAAs) are of particular interests. In this study, density functional theory (DFT) calculations and machine learning are employed to assess the effectiveness of Cu-, Ag-, and Ni-host SAAs as catalysts for electrochemical CO<sub>2</sub> reduction to CH<sub>3</sub>OH. The Gibbs free energies of 477 elementary reactions across 35 SAAs involved in CO<sub>2</sub> reduction are calculated, and by utilizing this dataset, a trained gradient boosting regression model is established with an excellent accuracy. Subsequently, the properties of 46 unknown SAAs are predicted, including their pathways, products, potential-determining steps (PDS), and corresponding Gibbs free energies of the PDS (<i>G</i><sub>PDS</sub>). Three promising candidates, ZnCu, AuAg and MoNi, stand out due to their lowest <i>G</i><sub>PDS</sub> among Cu-, Ag- and Ni- hosted SAAs, respectively.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"15 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Designing and screening single-atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning\",\"authors\":\"Wenyu Zhou, Haisong Feng, Shihong Zhou, Mengxin Wang, Yuping Chen, Chenyang Lu, Hao Yuan, Jing Yang, Qun Li, Luxi Tan, Lichun Dong, Yong-Wei Zhang\",\"doi\":\"10.1002/aic.18678\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Carbon dioxide (CO<sub>2</sub>) utilization technology is of great significance for achieving carbon neutrality, in which the catalytic materials play crucial roles, and among them, single-atom alloys (SAAs) are of particular interests. In this study, density functional theory (DFT) calculations and machine learning are employed to assess the effectiveness of Cu-, Ag-, and Ni-host SAAs as catalysts for electrochemical CO<sub>2</sub> reduction to CH<sub>3</sub>OH. The Gibbs free energies of 477 elementary reactions across 35 SAAs involved in CO<sub>2</sub> reduction are calculated, and by utilizing this dataset, a trained gradient boosting regression model is established with an excellent accuracy. Subsequently, the properties of 46 unknown SAAs are predicted, including their pathways, products, potential-determining steps (PDS), and corresponding Gibbs free energies of the PDS (<i>G</i><sub>PDS</sub>). Three promising candidates, ZnCu, AuAg and MoNi, stand out due to their lowest <i>G</i><sub>PDS</sub> among Cu-, Ag- and Ni- hosted SAAs, respectively.\",\"PeriodicalId\":120,\"journal\":{\"name\":\"AIChE Journal\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AIChE Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/aic.18678\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIChE Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/aic.18678","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Designing and screening single-atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning
Carbon dioxide (CO2) utilization technology is of great significance for achieving carbon neutrality, in which the catalytic materials play crucial roles, and among them, single-atom alloys (SAAs) are of particular interests. In this study, density functional theory (DFT) calculations and machine learning are employed to assess the effectiveness of Cu-, Ag-, and Ni-host SAAs as catalysts for electrochemical CO2 reduction to CH3OH. The Gibbs free energies of 477 elementary reactions across 35 SAAs involved in CO2 reduction are calculated, and by utilizing this dataset, a trained gradient boosting regression model is established with an excellent accuracy. Subsequently, the properties of 46 unknown SAAs are predicted, including their pathways, products, potential-determining steps (PDS), and corresponding Gibbs free energies of the PDS (GPDS). Three promising candidates, ZnCu, AuAg and MoNi, stand out due to their lowest GPDS among Cu-, Ag- and Ni- hosted SAAs, respectively.
期刊介绍:
The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering.
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