Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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Electrochemical reduction of carbon-dioxide/carbon-monoxide (CO(2)R) to fuels and chemicals presents an attractive approach for sustainable chemical synthesis, but it also poses a serious challenge in catalysis. Understanding the key aspects...

Electrochemical reduction of carbon dioxide (CO2) over transition metals follows a complex reaction network. Even for products with a single carbon atom (C1 products), two bifurcated pathways exist: initially between carboxyl (COOH*) and formate (HCOO*) intermediates and the COOH* intermediate is further bifurcated by pathways involving either formyl (CHO*) or COH*. In this study, we combine evidence from the experimental literature with a theoretical analysis of energetics to rationalize that not all steps in the reduction of CO2 are electrochemical. This insight enables us to create a selectivity map for two-electron products (carbon monoxide (CO) and formate) on elemental metal surfaces using only the CO and OH binding energies as descriptors. In the further reduction of CO * , we find that CHO* is formed through a chemical step only whereas COH* follows from an electrochemical step. Notably on Cu(100), the COH pathway becomes dominant at an applied potential lower than −0.5V vs. RHE. For the elemental metals selective towards CO formation, the variation of the CO binding energy is sufficient to further subdivide the map into domains that predominantly form H2, CO, and ultimately more reduced products. We find Cu to be the only elemental metal capable of reducing CO2 to products beyond 2e − via the proposed COH pathway and we identify atomic carbon as the key component leading to the production of methane. Our analysis also rationalizes experimentally observed differences in products between thermal and electrochemical reduction of CO2 on Cu.

First-row layered transition metal (oxy)(hydro)oxides (LTMOs) form an important class of earth-abundant materials. They are well-known as active alkaline oxygen evolution reaction (OER) catalysts, [1][2][3][4][5] and are also often used as metal-ion battery anodes [6] or as metal-air bifunctional electrodes. [7] However, their electrochemical activities, particularly for the oxygen reduction reaction (ORR), across the whole 3d-element series remain largely unexplored. In this work, we perform a systematic screening of these catalysts for both OER and ORR using a surface edge-site model with exposed active sites for metal double hydroxides M(OH) 2 , oxyhydroxides MOOH and oxides MO 2 . We establish OER and ORR activities and scaling relations of the whole series across + 2, + 3 and + 4 oxidation states, and successfully reproduce the experimental activities of a few pure layered (oxy)(hydro)oxides. We predict CoOOH/CoO 2 and NiOOH/NiO 2 as active and stable OER catalysts. We also predict Fe(OH) 2 /FeOOH, Mn(OH) 2 /MnOOH and Co(OH) 2 as active and stable ORR catalysts. This makes Co-(oxy)(hydro)oxides only bifunctional catalyst in this series. Using linear regression, our results indicate that trends across the 3d-series can be obtained from only a few bulk, surface and atomic type descriptors. Particularly, we identify that the number of outer d-electrons at the surface-active site as the most important descriptor of activity.

Accurate theoretical simulation of electrochemical activation barriers is key to understanding electrocatalysis and guides the design of more efficient catalysts. Providing a detailed picture of proton transfer processes encounters several challenges: the constant potential requirement during charge transfer, the different time scales involved in the processes, and the thermal fluctuation of the solvent. Hence, it is prohibitively expensive computationally to apply density functional theory (DFT) calculations in modeling the potential-dependent activation barrier at the electrode−solvent interface, and the results are dubious. To address these challenges, we have developed an analytical approach based on charge conservation and decoupled potential energy surfaces to compute charge transfer barriers. The method makes it possible to simulate an electrochemical process at different potentials and explicitly include thermal fluctuations of the solvent at the electrode−solvent interface. We use the Pt-catalyzed alkaline hydrogen evolution reaction (HER) as our benchmark reaction, and we model the microkinetics of HER with consideration of the spatial fluctuations between the metal surface and the first solvent layer at room temperature. The distribution of water−metal distances has a large effect on the barriers of the charge transfer processes, and an accurate account of the statistical fluctuation in the reaction network leads to a several orders of magnitude increase in HER current as compared to transfer from a static solvent. The trends of the different reaction mechanisms in HER were successfully simulated with our model, and the theoretical I−V curves obtained are in good qualitative agreement with experimental results.

Electrochemical reduction of carbon dioxide (CO2) over transition metals follows a complex reaction network. Even for products with a single carbon atom (C1 products), two bifurcated pathways exist: initially between carboxyl (COOH*) and formate (HCOO*) intermediates and the COOH* intermediate is further bifurcated by pathways involving either formyl (CHO*) or COH*. In this study, we combine evidence from the experimental literature with a theoretical analysis of energetics to rationalize that not all steps in the reduction of CO2 are electrochemical.This insight enables us to create a selectivity map for two-electron products (carbon monoxide (CO) and formate) on elemental metal surfaces using only the CO and OH binding energies as descriptors. In the further reduction of CO * , we find that CHO* is formed through a chemical step only whereas COH* follows from an electrochemical step. Notably on Cu(100), the COH pathway becomes dominant at an applied potential lower than −0.5V vs. RHE. For the elemental metals selective towards CO formation, the variation of the CO binding energy is sufficient to further subdivide the map into domains that predominantly form H2, CO, and ultimately more reduced products. We find Cu to be the only elemental metal capable of reducing CO2 to products beyond 2e − via the proposed COH pathway and we identify atomic carbon as the key component leading to the production of methane. Our analysis also rationalizes experimentally observed differences in products between thermal and electrochemical reduction of CO2 on Cu.

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key...