Rapid extraction of structure-property relationships is critical to the discovery of functional materials. One avenue to accelerate this process involves the use of machine learning (ML) models, which are becoming more reliable with the availability of libraries generated by high-throughput materials experiments and calculations . Using these libraries, data-driven techniques are now powerful enough that bulk structure-property relationships can be extracted from experimental X-ray diffraction data using automated agents .