{"title":"利用第一原理和机器学习方法探索不同电位下CO2还原反应的双原子催化剂","authors":"Haishan Yu, Lei Cui, DaDi Zhang","doi":"10.1007/s10562-025-05117-2","DOIUrl":null,"url":null,"abstract":"<div><p>Carbon dioxide (CO<sub>2</sub>) is a major greenhouse gas, contributing to global warming and climate change by raising surface temperatures and causing extreme weather events. This necessitates effective CO<sub>2</sub> removal and conversion into chemical products, aligning with the demands of energy materials science. Recent advancements in catalytic methods, particularly electrocatalysis, have focused on converting CO<sub>2</sub> into valuable fuels and industrial products using various catalysts. Among these, carbon-supported double atom catalysts (DACs) have demonstrated promise due to their close catalytic site proximity, enhancing electron transfer efficiency and stability. This study employs density functional theory (DFT) to explore the geometric configurations, electronic structures, and adsorption properties of 380 carbon-supported DACs for CO<sub>2</sub> reduction reactions (CO<sub>2</sub>RR). By analyzing reaction pathways involving HCOOH, CO, CH<sub>4</sub>/CH<sub>3</sub>OH, and hydrogen evolution reactions (HER) at 722 distinct active sites, we assess the performance and selectivity of these DACs across varying potentials. The integration of machine learning (ML) algorithms into the computational analyses allows for accurate predictions of intermediate adsorption energies and site selectivity, achieving a coefficient of determination (R<sup>2</sup>) of approximately 0.90 and a mean absolute error (MAE) of around 0.2. Ultimately, this comprehensive DFT-ML approach identifies several promising candidates for CO<sub>2</sub>RR and elucidates key descriptors that impact their performance.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":508,"journal":{"name":"Catalysis Letters","volume":"155 8","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploration of Dual-Atom Catalysts for CO2 Reduction Reaction Under Varying Potentials Using First-Principles and Machine Learning Approaches\",\"authors\":\"Haishan Yu, Lei Cui, DaDi Zhang\",\"doi\":\"10.1007/s10562-025-05117-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Carbon dioxide (CO<sub>2</sub>) is a major greenhouse gas, contributing to global warming and climate change by raising surface temperatures and causing extreme weather events. This necessitates effective CO<sub>2</sub> removal and conversion into chemical products, aligning with the demands of energy materials science. Recent advancements in catalytic methods, particularly electrocatalysis, have focused on converting CO<sub>2</sub> into valuable fuels and industrial products using various catalysts. Among these, carbon-supported double atom catalysts (DACs) have demonstrated promise due to their close catalytic site proximity, enhancing electron transfer efficiency and stability. This study employs density functional theory (DFT) to explore the geometric configurations, electronic structures, and adsorption properties of 380 carbon-supported DACs for CO<sub>2</sub> reduction reactions (CO<sub>2</sub>RR). By analyzing reaction pathways involving HCOOH, CO, CH<sub>4</sub>/CH<sub>3</sub>OH, and hydrogen evolution reactions (HER) at 722 distinct active sites, we assess the performance and selectivity of these DACs across varying potentials. The integration of machine learning (ML) algorithms into the computational analyses allows for accurate predictions of intermediate adsorption energies and site selectivity, achieving a coefficient of determination (R<sup>2</sup>) of approximately 0.90 and a mean absolute error (MAE) of around 0.2. Ultimately, this comprehensive DFT-ML approach identifies several promising candidates for CO<sub>2</sub>RR and elucidates key descriptors that impact their performance.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":508,\"journal\":{\"name\":\"Catalysis Letters\",\"volume\":\"155 8\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Catalysis Letters\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10562-025-05117-2\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Catalysis Letters","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10562-025-05117-2","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Exploration of Dual-Atom Catalysts for CO2 Reduction Reaction Under Varying Potentials Using First-Principles and Machine Learning Approaches
Carbon dioxide (CO2) is a major greenhouse gas, contributing to global warming and climate change by raising surface temperatures and causing extreme weather events. This necessitates effective CO2 removal and conversion into chemical products, aligning with the demands of energy materials science. Recent advancements in catalytic methods, particularly electrocatalysis, have focused on converting CO2 into valuable fuels and industrial products using various catalysts. Among these, carbon-supported double atom catalysts (DACs) have demonstrated promise due to their close catalytic site proximity, enhancing electron transfer efficiency and stability. This study employs density functional theory (DFT) to explore the geometric configurations, electronic structures, and adsorption properties of 380 carbon-supported DACs for CO2 reduction reactions (CO2RR). By analyzing reaction pathways involving HCOOH, CO, CH4/CH3OH, and hydrogen evolution reactions (HER) at 722 distinct active sites, we assess the performance and selectivity of these DACs across varying potentials. The integration of machine learning (ML) algorithms into the computational analyses allows for accurate predictions of intermediate adsorption energies and site selectivity, achieving a coefficient of determination (R2) of approximately 0.90 and a mean absolute error (MAE) of around 0.2. Ultimately, this comprehensive DFT-ML approach identifies several promising candidates for CO2RR and elucidates key descriptors that impact their performance.
期刊介绍:
Catalysis Letters aim is the rapid publication of outstanding and high-impact original research articles in catalysis. The scope of the journal covers a broad range of topics in all fields of both applied and theoretical catalysis, including heterogeneous, homogeneous and biocatalysis.
The high-quality original research articles published in Catalysis Letters are subject to rigorous peer review. Accepted papers are published online first and subsequently in print issues. All contributions must include a graphical abstract. Manuscripts should be written in English and the responsibility lies with the authors to ensure that they are grammatically and linguistically correct. Authors for whom English is not the working language are encouraged to consider using a professional language-editing service before submitting their manuscripts.