EZClust: A Robust Machine Learning-Based Powder X-Ray Diffraction and Raman Cluster Analysis Model for Efficient High-Throughput Crystallization Polymorph Screening

High-throughput crystallization (HTC) polymorph screening is pivotal for exploring the crystal polymorph landscape, but the sheer volume and complexity of powder X-ray diffraction (PXRD) and Raman spectroscopy data present significant data-processing challenges. Traditional approaches, which rely on human interpretation aided by software, are often constrained by limited clustering accuracy. To address these limitations, we developed EZClust, a lightweight machine-learning model designed for rapid PXRD and Raman batch data analysis. A key algorithm in the model is shape-based distance (SBD), which provides robust performance for processing data with distortion and minimal parameter tuning. In this work, we compare EZClust’s performance to existing mainstream commercial software (Jade Pro) and the open-source AutoFIDEL implementation, demonstrating its robustness through cluster analysis of HTC datasets for the model compounds ROY and carbamazepine. Herein, we disclose the core algorithms of EZClust, robust preprocessing coupled with an SBD metric, to streamline cluster analysis for PXRD and Raman datasets in HTC workflows.