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..., , , # Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts

This repository supports our *Science Advances* paper, “Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts,” (previous preprint on arXiv [https://arxiv.org/abs/2407.04877](https://arxiv.org/abs/2407.04877)) and serves as a roadmap to our data and code. The repository is organized into two main parts: Experimental Records, Raw Data, and Figures (released under CC0), Supplementay Notes (CC-BY) and the Machine Learning Scripts (released under the MIT License). All materials are available on Dryad (DOI: 10.5061/dryad.nk98sf83g) and GitHub ([https://github.com/ruiding-uchicago/DASH](https://github.com/ruiding-uchicago/DASH)).

### **Experimental Records, Raw Data, and Supplementary Figures**

**Overview:**
This repository contains experimental data—including raw electrochemica...