Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and...

A machine learning (ML) model is developed for predicting useable methane (CH₄) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH₄. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH₄ capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH₄ capacity for a prescribed operating condition.