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 of 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, exploiting 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.
ABSTRACT
Creating effective explosives with improved performance and physical properties is a challenging task. There are different methods to achieve this - creating completely new individual high-energy compounds or changing the characteristics of the already known ones. Cocrystallization is one of the ways to improve the critical properties of energetic materials. In this work we show that the crystal structure of stable molecular crystals and cocrystals of energetic molecules can be studied using the evolutionary algorithm USPEX coupled with forcefields or ab initio calculations. Here we show this through tests on PETN, TNT, HMX, CL-20, and TATB, and we separately consider the following compositions of cocrystals: DNDAP + CL-20 (4 : 8) and BTF + CL-20 (4 : 4). As a result, we found cocrytals of the previously known compositions and also novel cocrystals, which might also be stable in the experiment.
