Our work on multi-stage machine learning for catalyst discovery is now published in Science Advances.

The paper combines data mining, active learning, and domain adaptation to screen acidic OER electrocatalysts efficiently. Starting from literature-mined data, the pipeline narrows the candidate space through ML-guided selection, ending in experimentally validated high-performance materials.

Key results:

• Systematic navigation of multimetallic catalyst design space
• Experimental validation of ML-predicted candidates
• Demonstration of cross-domain transfer from alkaline to acidic conditions

Science Advances 2025, 11, eadr9038