A Machine Learning‐Enhanced Framework for the Accelerated Development of Spinel Oxide Electrocatalysts (Adv. Energy Mater. 39/2024)

Spinel Oxide Electrocatalysts In article number 2402342, Chan-Woo Lee, Kang Taek Lee, and co-workers present an artificial intelligence-enhanced computational framework for accelerating the development of spinel oxide electrocatalysts for green energy technologies. This work stands out amidst the challenge of data scarcity. The findings offer key insights into the mechanisms of electrochemical reactions, providing an accessible and undemanding descriptor for designing next-generation high-performance, durable electrocatalysts.