  We demonstrate a machine learning-based approach which predicts the
properties of crystal structures following relaxation based on the unrelaxed
structure. Use of crystal graph singular values reduces the number of features
required to describe a crystal by more than an order of magnitude compared to
the full crystal graph representation. We construct machine learning models
using the crystal graph singular value representations in order to predict the
volume, enthalpy per atom, and metal versus semiconducting phase of DFT-relaxed
organic salt crystals based on randomly generated unrelaxed crystal structures.
Initial base models are trained to relate 89,949 randomly generated structures
of salts formed by varying ratios of 1,3,5-triazine and HCl with the
corresponding volumes, enthalpies per atom, and phase of the DFT-relaxed
structures. We further demonstrate that the base model is able to extrapolate
to new chemical systems with the inclusion of 2,000 to 10,000 crystal
structures from the new system. After training a single model with a large
number of data points, extension can be done at significantly lower cost. The
constructed machine learning models can be used to rapidly screen large sets of
randomly generated organic salt crystal structures and efficiently downselect
the structures most likely to be experimentally realizable. The models can be
used either as a stand-alone crystal structure predictor or incorporated into
more sophisticated workflows as a filtering step.
