A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously

Abstract

A deep learning (DL)‐based approach for analysis is proposed. Using synthetic XRD data for a DL approach is inevitable due to the lack of real‐world XRD data. There are two main challenges when conducting a DL‐based XRD analysis: generating realistic XRD data including all possible perturbations, such as peak shift, broadening, texture, and noisy background, and generalizing the DL model applicability to all ICSD entries. To address both the perturbation and generalizability issues, a large‐scale computation is required because it would be infeasible with typical lab‐scale computation. Cloud computing infrastructures are leveraged for parallel computations and to obtain symmetry classification test accuracies of 98.95%, 97.18%, and 96.03% for the crystal system, extinction group, and space group, respectively. A stricter individual compound‐based train and test dataset‐splitting scheme also produces reasonable test accuracies of 92.25%, 87.34%, and 84.39%, which are still state‐of‐the‐art records. Crucially, the DL model trained on synthetic data is assessed using real‐world experimental XRD datasets to ensure its practical applicability. When tested on the real‐world experimental XRD dataset, the model achieves a test accuracy of 90.38% in predicting crystal systems.

Keywords

• cloud computing
• crystal structures
• deep learning
• fully convoluted networks
• X-ray diffraction analyses