Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

Effects of Metal Electrode Support on the Catalytic Activity of Fe(oxy)hydroxide for the Oxygen Evolution Reaction in Alkaline Media.

FeOx Hy and Fe-containing Ni/Co oxyhydroxides are the most-active catalysts for the oxygen evolution reaction (OER) in alkaline media. However, the activity of Fe sites appears strongly dependent on the electrode-substrate material and/or the elemental composition of the matrix in which it is embedded. A fundamental understanding of these interactions that modulate the OER activity of FeOx Hy is lacking. We report the use of cyclic voltammetry and chronopotentiometry to assess the substrate-dependent activity of FeOx Hy on a number of commonly used electrode substrates, including Au, Pt, Pd, Cu, and C. We also evaluate the OER activity and Tafel behavior of these metallic substrates in 1 M KOH aqueous solution with Fe3+ and other electrolyte impurities. We find that the OER activity of FeOx Hy varies by substrate in the order Au>Pd≈Pt≈Cu>C. The trend may be caused by differences in the adsorption strength of the Fe oxo ion on the substrate, where a stronger adhesion results in more adsorbed Fe at the interface during steady-state OER and possibly a decreased charge-transfer resistance at the FeOx Hy -substrate interface. These results suggest that the local atomic and electronic structure of [FeO6 ] units play an important role in catalysis of the OER as the activity can be tuned substantially by substrate interactions.