Acc Chem Res. 2025 Jul 21. doi: 10.1021/acs.accounts.5c00219. Online ahead of print.
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.
PMID:40690617 | DOI:10.1021/acs.accounts.5c00219