Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts

TL;DR

This paper presents a multi-stage machine learning framework that combines data mining, active learning, and domain adaptation to efficiently discover and optimize advanced acidic oxygen evolution electrocatalysts, exemplified by a novel Ru-Mn-Ca-Pr oxide.

Contribution

It introduces an integrated, multi-strategy ML approach that systematically accelerates electrocatalyst discovery, reducing reliance on trial-and-error and enhancing mechanistic understanding.

Findings

Discovered a promising Ru-Mn-Ca-Pr oxide catalyst.

Developed an efficient, data-driven discovery workflow.

Enhanced theoretical insights through domain adaptation.

Abstract

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.