Data-Driven Models for Predicting Stability of Electrocatalysts in Aqueous Environments

There is an increasing demand for alternative catalysts to platinum-group metals for the development of renewable energy technologies such as hydrogen fuel cells. A critical limitation to the performance of fuel cells is the electrochemical durability of the electrocatalytic electrodes. In this work, we implemented a computational framework to identify the factors that define the stability of metal electrodes in aqueous environments. To this end, we employed forward and inverse design with the goal of finding the most stable electrocatalysts using gradient boosting regression and Bayesian optimization. The dataset used to train the machine learning model comprises composition-based features as inputs and electrochemical decomposition energy as output. Maximum electronegativity and mode of covalent radius were identified as the most important features. The model can predict stability with an accuracy of 0.10 eV/atom and an explainability of 98%. This study provides a critical assessment of machine-learning models for estimating the electrochemical stability of catalytic alloys.