New Insights into Hydrogen Uptake on Porous Carbon Materials via Explainable Machine Learning

To understand hydrogen uptake in porous carbon materials, we developed machine learning models to predict excess uptake at 77 K based on the textural and chemical properties of carbon, using a dataset containing 68 different samples and 1745 data points. Random forest is selected due to its high performance (R² > 0.9), and analysis is performed using Shapley Additive Explanations (SHAP). It is found that pressure and Brunauer-Emmett-Teller (BET) surface area are the two strongest predictors of excess hydrogen uptake. Surprisingly, this is followed by a positive correlation with oxygen content, contributing up to ∼0.6 wt% additional hydrogen uptake, contradicting the conclusions of previous studies. Finally, pore volume has the smallest effect. The pore size distribution is also found to be important, since ultramicropores (dp < 0.7 nm) are found to be more positively correlated with excess uptake than micropores (d_p < 2 nm). However, this effect is quite small compared to the role of BET surface area and total pore volume. The novel approach taken here can provide important insights in the rational design of carbon materials for hydrogen storage applications.