Designing Anode Materials for Proton Exchange Membrane Electrolysis via Literature Data and Machine Learning

Proton exchange membrane (PEM) electrolysis is a crucial technology for sustainable hydrogen production, yet the discovery of efficient and durable anode materials remains a challenge. In this work, literature data on PEM electrolysis are systematically collected and analyzed to identify trends in material selection, synthesis methods, and key experimental conditions. Statistical analysis revealed that a limited number of anode and cathode materials dominate the field primarily due to their catalytic activity and stability in acidic environments. Supervised machine learning is then employed to predict anode materials aimed at reducing Ir content in the anode material by using compositional and experimental descriptors to model the cell performance. Experimental validation of Ir 0.9 M 0.1 O 2 (M = Cr, Zn, Sb, Ho) demonstrated that incorporating these predicted elements preserved cell performance while enabling a reduction in the Ir content at the anode. This study demonstrates that integrating data-driven approaches with literature analysis can effectively guide the discovery of anode materials, advancing the PEM electrolysis technology.