  One of the long-standing problems in materials science is how to predict a
material's structure and then its properties given only its composition.
Experimental characterization of crystal structures has been widely used for
structure determination, which is however too expensive for high-throughput
screening. At the same time, directly predicting crystal structures from
compositions remains a challenging unsolved problem. Herein we propose a deep
learning algorithm for predicting the XRD spectrum given only the composition
of a material, which can then be used to infer key structural features for
downstream structural analysis such as crystal system or space group
classification or crystal lattice parameter determination or materials property
predictions. Benchmark studies on two datasets show that our DeepXRD algorithm
can achieve good performance for XRD prediction as evaluated over our test
sets. It can thus be used in high-throughput screening in the huge materials
composition space for new materials discovery.
