Energy-Based Descriptors to Rapidly Predict Hydrogen Storage in Metal–Organic Frameworks

A machine-learning method was developed and used to predict hydrogen gas uptake in metal-organic frameworks (MOFs): the method has an accuracy within 3 g⋅L-1 and is more than three orders of magnitude faster than conventional molecular simulations.

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