Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal–Organic Frameworks for Hydrogen Storage

Metal–organic frameworks (MOFs) are one of the
most promising
hydrogen-storing materials due to their rich specific surface area,
adjustable topological and pore structures, and modified functional
groups. In this work, we developed automatically parallel computational
workflows for high-throughput screening of ∼11,600 MOFs from
the CoRE database and discovered 69 top-performing MOF candidates
with work capacity greater than 1.00 wt % at 298.5 K and a pressure
swing between 100 and 0.1 bar, which is at least twice that of MOF-5.
In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent
hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We
specifically analyzed the relationship between pore-limiting diameter,
largest cavity diameter, void fraction, open metal sites, metal elements
or nonmetallic atomic elements, and deliverable capacity and found
that not only geometrical and physical features of crystalline but
also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted
that we first proposed the modified crystal graph convolutional neural
networks by incorporating the obtained geometrical and physical features
into the convolutional high-dimensional feature vectors of period
crystal structures for predicting H2 storage performance,
which can improve the prediction accuracy of the neural network from
the former mean absolute error (MAE) of 0.064 wt % to the current
MAE of 0.047 wt % and shorten the consuming time to about 10–4 times of high-throughput computational screening. This work opens
a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and
discovery of other functional materials.