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Due to the subtle balance of intermolecular interactions that governstructure-property relations, predicting the stability of crystal structuresformed from molecular building blocks is a highly non-trivial scientificproblem. A particularly active and fruitful approach involves classifying thedifferent combinations of interacting chemical moieties, as understanding therelative energetics of different interactions enables the design of molecularcrystals and fine-tuning their stabilities. While this is usually performedbased on the empirical observation of the most commonly encountered motifs inknown crystal structures, we propose to apply a combination of supervised andunsupervised machine-learning techniques to automate the construction of anextensive library of molecular building blocks. We introduce a structuraldescriptor tailored to the prediction of the binding (lattice) energy and applyit to a curated dataset of organic crystals and exploit its atom-centerednature to obtain a data-driven assessment of the contribution of differentchemical groups to the lattice energy of the crystal. We then interpret thislibrary using a low-dimensional representation of the structure-energylandscape and discuss selected examples of the insights into crystalengineering that can be extracted from this analysis, providing a completedatabase to guide the design of molecular materials.

A data-driven interpretation of the stability of molecular crystals