A data-driven interpretation of the stability of molecular crystals

TL;DR

This paper uses machine learning to analyze and interpret the stability of molecular crystals, creating a comprehensive database to aid in crystal design by understanding chemical interactions and energetics.

Contribution

It introduces a novel structural descriptor and combines supervised and unsupervised learning to build an extensive library of molecular building blocks for crystal stability prediction.

Findings

Data-driven assessment of chemical group contributions to lattice energy

Low-dimensional structure-energy landscape representation

Insights into crystal engineering from machine learning analysis

Abstract

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials.