  Reticular materials, including metal-organic frameworks and covalent organic
frameworks, combine relative ease of synthesis and an impressive range of
applications in various fields, from gas storage to biomedicine. Diverse
properties arise from the variation of building units$\unicode{x2013}$metal
centers and organic linkers$\unicode{x2013}$in almost infinite chemical space.
Such variation substantially complicates experimental design and promotes the
use of computational methods. In particular, the most successful artificial
intelligence algorithms for predicting properties of reticular materials are
atomic-level graph neural networks, which optionally incorporate domain
knowledge. Nonetheless, the data-driven inverse design involving these models
suffers from incorporation of irrelevant and redundant features such as full
atomistic graph and network topology. In this study, we propose a new way of
representing materials, aiming to overcome the limitations of existing methods;
the message passing is performed on a coarse-grained crystal graph that
comprises molecular building units. To highlight the merits of our approach, we
assessed predictive performance and energy efficiency of neural networks built
on different materials representations, including composition-based and
crystal-structure-aware models. Coarse-grained crystal graph neural networks
showed decent accuracy at low computational costs, making them a valuable
alternative to omnipresent atomic-level algorithms. Moreover, the presented
models can be successfully integrated into an inverse materials design pipeline
as estimators of the objective function. Overall, the coarse-grained crystal
graph framework is aimed at challenging the prevailing atom-centric perspective
on reticular materials design.
