Solar cells and catalysis are two important applications in the field of renewable energy where the performances of the materials in these systems are influenced by the structure and stability of the electrodes. Here, we highlight the importance of understanding the dynamic processes in electrodes, such as electrochemical reactions and surface restructuring occurring before and under the catalytic process. To ultimately understand the structure-property relation, increased understanding of the actual catalyst structure at the reaction conditions are necessary. We will outline how operando Raman spectroscopy can be utilized to unveil electrocatalyst reformulations into the active catalyst phase [1,2] and their structural integrity [3, 4]. Apart from more conventional catalysts containing only a few elements, high-entropy alloys (HEAs), composed of five or more principal elements, provide a rich landscape of local atomic arrangements that can be tuned to create tailored catalytic sites with distinct electronic and geometric properties. The enormous combinatorial space of possible compositions and microstructures makes exhaustive experimental or purely first-principles exploration infeasible. Quantum-mechanics–guided AI and machine learning are therefore essential to predict key catalytic descriptors [5], prioritize promising compositions and electrolyte environments [6-7], spectroscopic features [8] and close the loop between high-throughput computation and targeted experiments to fully elucidate HEA design rules. We will exemplify this by showing how AI and machine learning can guide us to predict optimal composition in a Pt-Based HEA for electrocatalytic hydrogen evolution, validated experimentally over a wide pH range. The catalyst required less than 12 mV overpotential at a current density of 10 mA cm−2 under alkaline conditions and less than 9 mV under acidic conditions in repeated measurements, and a mass activity up to 16.69 A mg−1Pt [9].
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