ArticleScience advances2025
Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Orientation-Entropy-Mediated Derivative Cu Sites for Selective COAngewandte Chemie (International ed. in English) · 2026Article
- Machine learning-driven discovery of optimal designs for water electrolysis devices.Science advances · 2026Article
- Accelerated discovery of highly stable ruthenium-based high-entropy oxides for acidic oxygen evolution.Science advances · 2026Article
- Organic-Inorganic Metal Halide Perovskites: Toward Stability, Chirality, and AI-Guided Discovery.ACS central science · 2026Review
- Continuous discovery of novel 2D materials via dual active learning-driven generative models.National science review · 2026Article
- Hierarchical Embedded Sphere Model: An Interpretable ML-Guided Multiscale Descriptor Engineering Decodes OER Activity on TM@MOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization.Nanomaterials (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic 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 benchmark in electrocatalysts research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.