The horizontal camera scans a 2D barcode for sample registration at the beginning of the experiment, linking physical vials with digital plans and results. The other camera captures images from the bottom of the sample vials to monitor crystallization. We also incorporate a computer vision algorithm to identify the products in the vials. We trained the modified ResNet-18 algorithm on the photo dataset collected by our robotic arm. This classification model can accurately classify the conditions of the vial into three categories: liquid, single crystals and polycrystalline powders (Fig. ) with accuracy of 1.000, 0.875 and 0.917, respectively, giving an average accuracy on the test set of 0.925. Detailed information on dataset construction, data argumentation, model training procedures and the assessment of classification performance of the model are provided in the Supplementary Information Section 10.