{"title":"通过密度泛函理论和机器学习合理设计用于 NO-NH3 转化的铂锚定单原子合金电催化剂","authors":"","doi":"10.1016/S1872-2067(24)60078-1","DOIUrl":null,"url":null,"abstract":"<div><p>Electrochemical NO reduction reaction (NORR) toward NH<sub>3</sub> synthesis emerges as a promising approach to eliminate NO pollution and generate high-value-added products simultaneously. Therefore, exploring suitable NORR electrocatalysts is of great importance. Here, we present a design principle to evaluate the activity of single-atom alloy catalysts (SAACs), whose excellent catalytic performance and well-defined bonding environments make them suitable candidates for studying structure-activity relationships. The machine learning (ML) algorithm is chosen to unveil the underlying physics and chemistry. The results indicate that the catalytic activity of SAACs is highly correlated with the local environment of the active center, that is, the atomic and electronic features. The coeffect of these features is quantitatively verified by adopting a data-driven method. The combination of density functional theory (DFT) and ML investigations not only provides an understanding of the complex NORR mechanisms but also offers a strategy to design highly efficient SAACs with specific active centers rationally.</p></div>","PeriodicalId":9832,"journal":{"name":"Chinese Journal of Catalysis","volume":null,"pages":null},"PeriodicalIF":15.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning\",\"authors\":\"\",\"doi\":\"10.1016/S1872-2067(24)60078-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Electrochemical NO reduction reaction (NORR) toward NH<sub>3</sub> synthesis emerges as a promising approach to eliminate NO pollution and generate high-value-added products simultaneously. Therefore, exploring suitable NORR electrocatalysts is of great importance. Here, we present a design principle to evaluate the activity of single-atom alloy catalysts (SAACs), whose excellent catalytic performance and well-defined bonding environments make them suitable candidates for studying structure-activity relationships. The machine learning (ML) algorithm is chosen to unveil the underlying physics and chemistry. The results indicate that the catalytic activity of SAACs is highly correlated with the local environment of the active center, that is, the atomic and electronic features. The coeffect of these features is quantitatively verified by adopting a data-driven method. The combination of density functional theory (DFT) and ML investigations not only provides an understanding of the complex NORR mechanisms but also offers a strategy to design highly efficient SAACs with specific active centers rationally.</p></div>\",\"PeriodicalId\":9832,\"journal\":{\"name\":\"Chinese Journal of Catalysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Journal of Catalysis\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1872206724600781\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Catalysis","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1872206724600781","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
摘要
为合成 NH3 而进行的电化学 NO 还原反应(NORR)是消除 NO 污染并同时生成高附加值产品的一种前景广阔的方法。因此,探索合适的 NORR 电催化剂非常重要。单原子合金催化剂(SAACs)具有优异的催化性能和明确的键合环境,是研究结构-活性关系的合适候选材料,在此,我们提出了一种评估单原子合金催化剂(SAACs)活性的设计原理。研究选择了机器学习(ML)算法来揭示其背后的物理和化学原理。结果表明,SAACs 的催化活性与活性中心的局部环境,即原子和电子特征高度相关。采用数据驱动方法定量验证了这些特征的协同效应。密度泛函理论(DFT)与 ML 研究的结合不仅有助于理解复杂的 NORR 机理,还为合理设计具有特定活性中心的高效 SAAC 提供了一种策略。
Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning
Electrochemical NO reduction reaction (NORR) toward NH3 synthesis emerges as a promising approach to eliminate NO pollution and generate high-value-added products simultaneously. Therefore, exploring suitable NORR electrocatalysts is of great importance. Here, we present a design principle to evaluate the activity of single-atom alloy catalysts (SAACs), whose excellent catalytic performance and well-defined bonding environments make them suitable candidates for studying structure-activity relationships. The machine learning (ML) algorithm is chosen to unveil the underlying physics and chemistry. The results indicate that the catalytic activity of SAACs is highly correlated with the local environment of the active center, that is, the atomic and electronic features. The coeffect of these features is quantitatively verified by adopting a data-driven method. The combination of density functional theory (DFT) and ML investigations not only provides an understanding of the complex NORR mechanisms but also offers a strategy to design highly efficient SAACs with specific active centers rationally.
期刊介绍:
The journal covers a broad scope, encompassing new trends in catalysis for applications in energy production, environmental protection, and the preparation of materials, petroleum chemicals, and fine chemicals. It explores the scientific foundation for preparing and activating catalysts of commercial interest, emphasizing representative models.The focus includes spectroscopic methods for structural characterization, especially in situ techniques, as well as new theoretical methods with practical impact in catalysis and catalytic reactions.The journal delves into the relationship between homogeneous and heterogeneous catalysis and includes theoretical studies on the structure and reactivity of catalysts.Additionally, contributions on photocatalysis, biocatalysis, surface science, and catalysis-related chemical kinetics are welcomed.