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Single-atom alloys (SAAs) arise as a promising concept for the design of improved CO2 hydrogenation catalysts. However, from the immense number of possible SAA compositions and structures, only a few might display the properties required to be useful catalysts. Thus, the direct, high-throughput screening of materials is inefficient. Here, we use artificial intelligence to derive rules describing surface sites of SAAs that provide an effective CO2 activation, a crucial initial step to convert the molecule into valuable products. We start by modeling the CO2 interaction with 780 sites of flat and stepped surfaces of SAAs composed by Cu, Zn, and Pd hosts via high-quality DFT-mBEEF calculations. Then, we apply subgroup discovery to determine constraints on key physicochemical properties, out of 24 offered candidate descriptive parameters, characterizing subgroups (SGs) of surface sites where chemisorbed CO2 displays large elongations of its C–O bonds. The key identified parameters are free-atom properties of the elements constituting the surface sites, such as their electron affinity, electronegativity, and radii of the d-orbitals. Additionally, the generalized coordination number is selected as a key geometrical parameter. The SG rules are applied to identify promising surface sites from a candidate space of over 1500 possible ones in different single-atom and dual-atom alloys. Some of the promising alloys predicted by the SG rules were explicitly tested by additional DFT-mBEEF calculations and confirmed to provide a significant CO2 activation.
Rivera-Arrieta et al. (Tue,) studied this question.