  Developing advanced catalysts for acidic oxygen evolution reaction (OER) is
crucial for sustainable hydrogen production. This study introduces a novel,
multi-stage machine learning (ML) approach to streamline the discovery and
optimization of complex multi-metallic catalysts. Our method integrates data
mining, active learning, and domain adaptation throughout the materials
discovery process. Unlike traditional trial-and-error methods, this approach
systematically narrows the exploration space using domain knowledge with
minimized reliance on subjective intuition. Then the active learning module
efficiently refines element composition and synthesis conditions through
iterative experimental feedback. The process culminated in the discovery of a
promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical
simulations with domain adaptation strategy, providing deeper mechanistic
insights aligned with experimental findings. By leveraging diverse data sources
and multiple ML strategies, we establish an efficient pathway for
electrocatalyst discovery and optimization. This comprehensive, data-driven
approach represents a paradigm shift and potentially new benchmark in
electrocatalysts research.
