An interpretable and explainable deep learning model for predicting hydrogen solubility in diverse chemicals

Abstract

In this study, an explainable and interpretable deep learning (DL) model based on convolutional neural network (CNN) was suggested to accurately estimate H₂ solubility in various chemicals under vast ranges of pressure and temperature. The model was implemented using more than 3700 authenticated datapoints. The results revealed that the CNN model achieved excellent predictions and surpassed the well-known machine learning (ML) and prior predictive paradigms. In this context, the CNN demonstrated attractive statistical metrics (RMSE = 0.0049 and R² = 0.9934). The explainability and interpretability of the suggested DL-based model were testified using the Shapley Additive exPlanations (SHAP) method. Additionally, trend analyses were conducted on the model’s predictions to verify that it accurately reflects H₂ solubility trends in various chemicals at different pressure and temperature levels. Lastly, the capability of the introduced DL model greatly improves the simulation of processes involving this crucial parameter in both industrial and academic sectors.