Controlling metal–organic framework crystallization via computer vision and robotic handling

Abstract

Traditional experimental techniques for metal–organic frameworks (MOFs) crystal growth are often time-consuming due to the need for manual bench chemistry and data analysis. In this study, we integrated laboratory automation with computer vision to accelerate the synthesis and characterization of Co-MOF-74, a porous framework containing coordinatively unsaturated Co(II) sites. By utilizing a liquid-handling robot, we significantly improved the efficiency of precursor formulation for solvothermal synthesis, saving approximately one hour of manual hands-on labor per synthesis cycle. We developed an accelerated characterization strategy using high-throughput optical microscopy and computer vision to identify the quality of crystallization outcomes. Our computer vision framework, Bok Choy Framework, enabled automated feature extraction from microscopic images, improving the analysis efficiency by approximately 35 times compared to manual analysis methods. Using this integrated workflow, we systematically performed a rapid screening of synthesis parameters and examined how each parameter influenced the crystal morphology. Furthermore, by varying solvent compositions, we rapidly screened synthesis conditions that modulated crystal formation, identifying regimes that promoted crystallization or inhibit growth. The resulting structured dataset linking synthesis conditions to crystal morphology provided a scalable foundation for data-driven materials discovery. The combination of automated experimentation and data analysis establishes a cost-effective and widely applicable platform for accelerating research of functional materials, with broad applications in catalysis, energy storage, and beyond.