Machine learning-guided optimization of high-performance porous composite membranes for alkaline water electrolysis

• Machine learning models are developed to predict the performance of porous composite separators in alkaline water electrolysis for hydrogen production. • SHAP method is used to analyze the impact of membrane manufacturing conditions on performance. • The interaction SHAP method is used to evaluate the interactions among the membrane manufacturing conditions. • It is highly targeted to experimentally validate high performance membranes by grid-based prediction combined with manual adjustment of process parameters. The production of green hydrogen via alkaline water electrolysis necessitates porous composite membranes with high ionic conductivity and high bubble-point pressure. However, the mainstream preparation process of porous composite membranes involves many parameters, rendering this a complex high-dimensional optimization problem. Traditional trial-and-error experimentation is inefficient and often fails to explore the performance boundaries. In this study, an XGBoost -based machine learning model is developed and trained with laboratory-collected datasets, achieving satisfactory predictive performance. The model provides critical insights into the relationships between six manufacturing parameters and the two core performance parameters of the membrane. Subsequently, prediction based on coarse-grained grid and reverse search are performed on the model to identify optimal parameter regions, followed by manual refinement through feature analysis. This integrated approach ultimately identifies three high-performance composite membrane candidates, which are experimentally validated. This work demonstrates a highly efficient and accurate machine learning-driven paradigm for the development of advanced porous composite membrane in alkaline water electrolysis.