DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition

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 prediction. Benchmark studies on two data sets 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 materials discovery.

Automated summary

This study proposes a deep learning algorithm for predicting the XRD spectrum based on the composition of a material, which can be used for structural analysis and property prediction. The algorithm shows promising performance in XRD prediction and has potential applications in high-throughput materials screening.