Energy

Predicting Hydrogen Storage in MOFs via Machine Learning

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Abstract

The H2 storage capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials under physisorptive conditions. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (> 5,300 m2 g-1), void fractions (~0.90), and pore volumes (>3.3 cm3 g-1). In addition, the relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The single most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available for use via the web, allowing for rapid and accurate predictions of usable hydrogen capacities for MOFs with only minimal structural data as input; for the simplest models only a single input feature is required.

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Supplementary material

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