Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence

Abstract

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants’ facile dissociation and a balanced strength of intermediates’ binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.

Going Beyond the d‐Band Center to Describe CO2 Activation on Single‐Atom Alloys

“…Nevertheless, it is evident that the provided linear correlation predicted by the d‐band center model fails for CO 2 molecule uptake on SAAs. The model relations also do not hold for the adsorption of other species catalyzed by SAAs [16a] …”

“…Compared with previous work, our method of calculating the d‐band center that projects to the guest atom provides better correlations with other properties than calculating the d‐band center projected on 1) the guest atom and its first adjacent host atoms or 2) the entire slabs. [ 16 ] When E d is less than ≈−2.0 eV, the “phys” model is prevalent in all SAAs, with the quasilinear form by an E ads of ≈−0.2 eV, while in the region above an E d of −0.5 eV, the SAAs are likely to capture CO 2 molecules with the bent configuration. However, the SAAs inside the green‐highlighted region of Figure 3a with E d values in the range of −2.0−0.5 eV exhibit various “phys” models, as well as a few “chem” models.…”

“…This implies that the inclusion of the guest atom plays a crucial role in determining E ads , especially φ g , which has also proved to be a significant factor for the bonding strength. [ 17 ] Table S3 and S4, Supporting Information, show the correlations for the top ten 1D descriptors identified by the first and second iterations of feature constructions (Φ 1 and Φ 2 ), respectively. Notably, these descriptors exhibit better correlation with E ads than the best primary feature ( φ g ) and can be easily obtained via simple algebraic operations on the base parameters.…”

Understanding Single-Atom Catalysis in View of Theory

“… 86 Anyway, charge, coordination number, and interactions with substrates will all affect the activities of SACs in thermal reactions. First-principles simulations combined with statistical learning 87 and artificial intelligence 88 have shown great advantages in describing the properties of thermal SACs and will play a more important role in future works.…”

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

“…ANN is used previously to predict hydrogen atom binding energy on SAAs using simple properties from periodic table. [6] This approach resulted in an RMSE of around 0.1 eV using periodic properties of host and single atoms. In another example set by Nørskov and coworkers, [62] adsorption energy of CO on bimetallic alloys at different sites was predicted using neural networks but with a huge dataset of around 70,000 DFT datapoints.…”