Bayesian learning-assisted catalyst discovery for efficient iridium utilization in electrochemical water splitting.

Reducing noble metal dependence in oxygen evolution reaction (OER) catalysts is essential for achieving sustainable and scalable green hydrogen production. Bimetallic oxides, with their potential for high catalytic performance and reduced noble metal content, represent promising alternatives to traditional IrO-based OER catalysts. However, optimizing these materials remains challenging due to the complex interplay of elemental selection, composition, and chemical ordering. In this study, we integrate density functional theory (DFT) calculations with Bayesian learning to accelerate the discovery of high-performance, low-Ir bimetallic oxides, identifying surface Ir-doped TiO as an optimal catalyst. Guided by theoretically optimized surface compositions and oxygen vacancies, we synthesized atomically dispersed Ir on TiO, achieving a 23-fold increase in Ir mass-specific activity and a 115-millivolt reduction in overpotential compared to commercial IrO. This work exemplifies a sustainable, data-driven pathway for electrocatalyst design that minimizes noble metal usage while maximizing efficiency, advancing scalable solutions in renewable energy and hydrogen production.