Innovative application of transfer learning on small-scale datasets: Analysis and optimization of catalyst ink for the low-iridium membrane electrode assemblies of proton exchange membrane water electrolysis

Abstract

The scarcity of Ir poses a significant barrier to the widespread adoption of proton exchange membrane water electrolysis (PEMWE), necessitating minimization of Ir loading in membrane electrode assemblies (MEAs). This study introduced a transfer learning strategy to optimize catalyst ink formulations for performance enhancement of low-Ir MEAs. We leveraged a pre-trained model from previous research and fine-tuned it on a small dataset of 12 MEAs’ ink preparation data, which significantly reduced labor, time, and costs associated with extensive experimentation. The developed model demonstrated high accuracy with an R2 of 0.99507. The parameters of the ink formulation were assessed using the Shapley Additive exPlanations method and optimized through the Harris Hawk Optimization algorithm. This optimization yielded an electrolysis voltage of 1.898 V at 3.0 A cm−2 for MEAs with 0.15 mg cm−2 Ir loading. These performance improvements are attributed to enhanced catalyst-ionomer particle dispersion and optimized rheological properties of the ink, which lead to more uniform distribution of Ir in the catalytic layers. These results demonstrate the feasibility and efficacy of transfer learning techniques to develop high-performance models from limited datasets, offering a promising pathway for accelerated materials discovery and optimization in the field of PEMWE.