  A fundamental problem in applying machine learning techniques for chemical
problems is to find suitable representations for molecular and crystal
structures. While the structure representations based on atom connectivities
are prevalent for molecules, two-dimensional descriptors are not suitable for
describing molecular crystals. In this work, we introduce the SFC-M family of
feature representations, which are based on Morton space-filling curves, as an
alternative means of representing crystal structures. Latent Semantic Indexing
(LSI) was employed in a novel setting to reduce sparsity of feature
representations. The quality of the SFC-M representations were assessed by
using them in combination with artificial neural networks to predict Density
Functional Theory (DFT) single point, Ewald summed, lattice, and many-body
dispersion energies of 839 organic molecular crystal unit cells from the
Cambridge Structural Database that consist of the elements C, H, N, and O.
Promising initial results suggest that the SFC-M representations merit further
exploration to improve its ability to predict solid-state properties of organic
crystal structures

Machine Learning;Chemical Physics