Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME

In this work, as an extension of previous machine learning studies, three novel techniques, namely local interpretable model-agnostic explanations (LIME), neural network pattern recognition and association rule mining (ARM) were utilized for proton exchange membrane (PEM) and anion exchange membrane (AEM) electrolyzer database for hydrogen production. The main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density, power density and polarization. Using this technique, it was possible to uncover rules or paths that lead to high current density, low power density and low polarization. ARM provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer, using pure Pt on the cathode surface and using pure carbon as the cathode support. In addition, LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cathode/anode support/surface elements, operational variables like K2CO3 or KOH concentration in the electrolyte, certain membrane types, gas diffusion layers, and applied potential on current density. It was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation, which can also guide future research.