Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: an AI Approach Integrating Theoretical and Experimental Data

Designing materials for catalysis is challenging because the performance is governed by an intricate interplay of various multi-scale phenomena, such as chemical reactions on surfaces and the materials’ restructuring during the catalytic process. In the case of supported catalysts, the role of the support material can be also crucial. Here, we address this intricacy challenge by a symbolic-regression artificial-intelligence approach. We identify the key physicochemical parameters correlated with the measured performance, out of many offered candidate parameters characterizing the material, reaction environment, and possibly relevant underlying phenomena. Importantly, these parameters are obtained by both experiments and ab initio simulations. The identified key parameters might be called “materials genes”, in analogy of genes in biology: they correlate with the property or function of interest, but the explicit physical relationship is not (necessarily) known. To demonstrate the approach, we investigate CO2 hydrogenation to CH3OH catalyzed by cobalt nanoparticles supported on silica. Crucially, the silica support is modified with the additive metals magnesium, calcium, titanium, aluminum, and zirconium, which results in six materials with significantly different performances. These systems mimic hydrothermal vents, which might have produced the first organic molecules on Earth. The key parameters correlated with the CH3OH selectivity reflect the reducibility of cobalt species, adsorption strength of reaction intermediates, and the chemical nature of the additive metal. By using SISSO model trained on elemental properties of the additive metals (e.g., ionization potential), new additives are suggested. The predicted CH3OH selectivity of catalysts with vanadium and zinc was confirmed by new experiments.