Structural Descriptor Bridging the Microstructural Feature and Catalytic Reactivity for Rational Design of Metal Catalysts.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Haoxiang Xu, Jiayi Wang, Jin Liu, Daojian Cheng
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引用次数: 0

Abstract

ConspectusMetal heterogeneous catalysis is the workhorse of the chemical industry, driving the conversion of reactants to desirable products. Traditional design approaches for metal catalysts rely on trial-and-error tests and take a lot of time to identify promising catalytic active species from the large candidate space. Over the decades, much focus has been placed on identifying the factors affecting the active sites, which, in turn, guides the design and preparation of more active, selective, and stable catalysts. In the context of theoretical design method for catalysts, the concept of the energy descriptor strategy provides correlations between the adsorption energy of key intermediates and catalytic reactivity. Such energy descriptors for catalytic reactivity can be used to predict the activity of candidate catalysts and understand trends among different catalysts.However, a more efficient descriptor strategy is still attractive and needed that avoids density functional theory calculation on the adsorption energy of each candidate and possesses the guidance power for the rational design of microstructural characteristics of catalytic active species. In this regard, bridging the gap between the electronic/atomic-level descriptions of the microscopic properties of the catalytic active species and the macroscopic catalytic performance of the desirable reaction, that is, the microscopic-to-macroscopic relationship, remains intriguing yet challenging, toward which progress leads to revolutionizing catalyst design.In this Account, we propose a structural descriptor strategy that for the first time maps the quantitative relationship between microstructural features and catalytic performances for metal catalysts, as well as its application in the high-throughput screening and rational design of catalytic active species. We begin with the analysis of the microstructural characteristics of the reaction center and its coordination environment and extract key feature parameters to build a mathematical expression of the structural descriptor. Next, through regression fitting, a mathematical correlation is built between the structural descriptor and the energetics involved with the reaction pathway. Finally, substituting the above statistical correlations into the rate equation derived from microkinetic model offers the structural descriptor-based prediction model for metal catalysts. The use of easily accessible structural descriptors has proven to be a powerful method to advance and accelerate the discovery and design of metal catalysts, including atomically dispersed metal catalysts, metal alloy catalysts, and metal cluster catalysts. Overall, the structural descriptor strategy not only demonstrates much potential to elucidate the quantitative interplay between microstructural features of catalytic active species and intrinsic catalytic reactivity but also provides a new approach in kinetics analysis to rationalize metal catalyst design. We conclude with an outlook for further constructing a universal structural descriptor and accelerating predictions on catalytic performance of metal catalysts by leveraging material databases and machine learning.

结构描述子桥接微观结构特征和催化反应性为金属催化剂的合理设计。
金属多相催化是化学工业的主力,推动反应物转化为理想的产物。传统的金属催化剂设计方法依赖于反复试验,需要花费大量时间从大量的候选空间中确定有希望的催化活性物质。几十年来,人们一直把重点放在确定影响活性位点的因素上,这反过来又指导设计和制备更具活性、选择性和稳定性的催化剂。在催化剂理论设计方法的背景下,能量描述符策略的概念提供了关键中间体吸附能与催化反应活性之间的相关性。这种催化活性的能量描述符可以用来预测候选催化剂的活性,并了解不同催化剂之间的趋势。然而,一种更有效的描述子策略仍然是有吸引力的,并且需要避免对每个候选物质的吸附能进行密度泛函理论计算,并对合理设计催化活性物质的微观结构特征具有指导作用。在这方面,弥合催化活性物质微观性质的电子/原子级描述与理想反应的宏观催化性能之间的差距,即微观到宏观的关系,仍然是有趣但具有挑战性的,这方面的进展导致了革命性的催化剂设计。在本文中,我们首次提出了一种结构描述子策略,该策略绘制了金属催化剂的微观结构特征与催化性能之间的定量关系,并将其应用于催化活性物质的高通量筛选和合理设计。首先分析反应中心及其配位环境的微观结构特征,提取关键特征参数,建立结构描述符的数学表达式。其次,通过回归拟合,在结构描述符和反应途径所涉及的能量之间建立数学相关性。最后,将上述统计相关性代入由微动力学模型导出的速率方程,得到了基于结构描述符的金属催化剂预测模型。使用易于获取的结构描述符已被证明是推进和加速金属催化剂的发现和设计的有力方法,包括原子分散金属催化剂、金属合金催化剂和金属团簇催化剂。总体而言,结构描述子策略不仅在阐明催化活性物质的微观结构特征与本征催化反应性之间的定量相互作用方面具有很大的潜力,而且还为动力学分析提供了一种新的方法来合理化金属催化剂的设计。最后,我们展望了利用材料数据库和机器学习进一步构建通用结构描述符和加速预测金属催化剂的催化性能。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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