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.