(449c) Predicting Hydrogen Storage in Half-a-Million MOFs Via Machine Learning

Metal-organic frameworks (MOFs) are promising solid-state adsorbents thanks to their high gravimetric capacities. However, realizing a high volumetric H₂ adsorption capacity, balanced with a high gravimetric density, is one of the main barriers of the successful application of MOFs as solid-state adsorbents. A large database of half-a-million MOFs consisting of real and hypothetical compounds was compiled and screened using semi-empirical and atomistic (grand canonical Monte Carlo) techniques. Several machine learning (ML) algorithms were benchmarked for their ability to predict hydrogen storage in MOFs at multiple conditions. The top performing algorithm, extremely randomized trees, was applied to rapidly identify MOFs with high usable H₂ storage capacities across the entire database. A combinatorial approach was then used to understand the importance of crystallographic properties and training set size. This approach identifies the number and combination of crystallographic features needed to achieve the most accurate predictions. Finally, we assess multilinear regression models for their ability to out-perform the well-known Chahine rule for predicting H₂ uptake.