High‐throughput X‐ray diffraction (XRD) techniques are revolutionizing materials science by enabling the rapid collection of hundreds or thousands of diffraction patterns in a single experiment. However, traditional analytical methods like Rietveld refinement become a significant bottleneck for quantifying phase fractions from such large datasets, due to their time‐consuming, expert‐driven nature. In this work, we develop a convolutional neural network (CNN) approach to directly quantify phase fractions in two‐phase steels using only experimental XRD data. The model is trained on over 40 000 real XRD patterns obtained from a high‐throughput synchrotron campaign spanning a wide range of steel compositions, heat treatments, and measurement configurations. The CNN learns to map 1D diffraction patterns to phase fraction outputs, effectively bypassing the need for iterative profile fitting. On test data, the model achieves excellent agreement with ground‐truth phase fractions (obtained via conventional analysis), with mean absolute errors on the order of 2%. The inference speed (milliseconds per pattern) is almost two orders of magnitude faster than Rietveld refinement, enabling real‐time phase monitoring during high‐volume experiments. This CNN‐based method demonstrates a scalable, accurate alternative for phase quantification in high‐throughput settings, addressing the critical need for rapid data analysis in modern materials research.
Benrabah et al. (Wed,) studied this question.