In high‐throughput applications, crystallographic analysis via X‐ray diffraction (XRD) is often limited by long exposure times and the need for manual data interpretation. This study presents a novel machine‐learning‐based approach for volume fraction estimation from XRD patterns addressing both challenges. The method efficiently processes noisy XRD patterns acquired with polychromatic emission spectra, and enables volume fraction estimation from thousands of patterns per second. Compared to conventional techniques, it requires much lower XRD pattern quality, allowing for shorter exposure times. This makes our method particularly suited for high‐throughput scenarios such as self‐driving labs. We utilize simulated XRD patterns to train two neural networks to process XRD data in a specified material system. A convolutional neural network (CNN) estimates volume fractions, while a u‐net‐style network restores pattern interpretability by resolving peak duplication caused by polychromatic emission spectra. We use synthetic datasets to showcase the method's noise tolerance and ability to analyze XRD patterns with multiple emission lines. Furthermore, we verify our method's applicability to real XRD patterns using a small experimental dataset.
Hofer et al. (Wed,) studied this question.