Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective

Abstract Heterogenous electrocatalysis continues to witness propagating interest in a plethora of non‐limiting electrochemical fields. Of which, water electrolysis has moved from lab‐scale systems to commercial electrolyzers albeit high dependence on historic benchmark noble‐metal based catalysts is still the status quo. Notwithstanding, advances in material groups such as single‐atom catalysts, perovskites, high‐entropy alloys, among others continue to see an increased interest toward utilization in next‐generation electrolyzers. To that end, progress in electrocatalyst discovery techniques is revolutionized through synergistically combining density functional theory (DFT) and machine learning (ML) techniques. The success of ML herein depends on numerous interlinked factors such as the algorithm employed, data availability and accuracy, with descriptors being critical to encapsulate physicochemical perspectives. Historic utilization of ML frameworks in areas other than materials discovery has left a lack of standardization toward appropriating suitable methods of high‐throughput DFT, ML approaches, and feature engineering that bridge the gap between activity‐structure‐electronic relationships. This review outlines needed considerations toward DFT calculations, important criteria during filtering out screened surfaces, and synergistic approaches toward utilizing theoretical and/or experimental datasets for formulating effective ML frameworks. Persisting challenges, perspectives, and recommendations thereof are highlighted to expedite and generalize future work pertaining to high‐volume water electrocatalysis discovery.