Data from: Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER)
is crucial for sustainable hydrogen production. This study presents a
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
demonstrate an efficient pathway for electrocatalyst discovery and
optimization. This comprehensive, data-driven approach represents a
paradigm shift and potentially a benchmark in electrocatalysts research.