CrystalCV: A Computer Vision System for Analysis of Crystallization Experiments

Abstract

Understanding and controlling single crystal growth is critical for synthesizing high-quality, defect-free materials like metal halide perovskites; however, monitoring these processes is hindered by a lack of tools capable of persistently tracking multiple crystals. We present crystallization computer vision (CrystalCV), an accessible, Python-based computer vision system for monitoring crystallization processes. Using color segmentation and centroid tracking, the CrystalCV continuously extracts time-series data for multiple crystals. We demonstrate its utility through three case studies in single crystal metal halide perovskite crystallization: quantifying MAPbBr3 and CsPbBr3 growth rate uniformity, validating reactive thermal control to eliminate secondary nucleations, and tracking phase evolution in FAPbI3 thin crystals. CrystalCV provides a versatile, low-barrier platform for characterizing and optimizing complex crystal growth.