  We develop and test new machine learning strategies for accelerating
molecular crystal structure ranking and crystal property prediction using tools
from geometric deep learning on molecular graphs. Leveraging developments in
graph-based learning and the availability of large molecular crystal datasets,
we train models for density prediction and stability ranking which are
accurate, fast to evaluate, and applicable to molecules of widely varying size
and composition. Our density prediction model, MolXtalNet-D, achieves state of
the art performance, with lower than 2% mean absolute error on a large and
diverse test dataset. Our crystal ranking tool, MolXtalNet-S, correctly
discriminates experimental samples from synthetically generated fakes and is
further validated through analysis of the submissions to the Cambridge
Structural Database Blind Tests 5 and 6. Our new tools are computationally
cheap and flexible enough to be deployed within an existing crystal structure
prediction pipeline both to reduce the search space and score/filter crystal
candidates.
