Edward C Lee, Daniel Salley, Abhishek Sharma, Leroy Cronin
Crystallisation is central to purification and to determining structure and material properties, yet small changes in conditions can produce many different polymorphs with distinct behaviours. Because crystallisation depends on multiple variables including solvent, temperature, pressure, and atmosphere and often proceeds unpredictably, mapping these outcomes is slow and expensive. Here we introduce a robotic crystal search engine that explores crystallisation space efficiently and autonomously. The platform couples high-throughput liquid handling with a closed-loop computer-vision system combined with human supervision that uses machine learning to detect crystals, distinguish polymorphs, and identify previously unseen forms. Using a benchmark polymorphic compound, we show that the robot can rapidly navigate a high-dimensional solvent space, quantify relative polymorph yields directly from images, and build a phase diagram without recourse to crystallography. This approach reveals the full set of polymorphs accessible under given conditions and identifies the optimal conditions for producing each one.