Computer vision based crystallization monitoring in automated laboratories

Research into new functional materials has been ongoing on for many years. The successes are based on a classic trialand-error method. In the years that followed, various methods such as computer-aided calculations and high-throughput screening were added. Since the beginning of the 21st century, immense progress has been made in the field of artificial intelligence, which has since found its way into a wide variety of specialist disciplines and everyday life. With the advent of artificial intelligence in the research of new materials, there is hope for new results and savings in time and money. The approach presented here serves to monitor crystallization processes. Crystallization processes are used to evaporate new compositions of substances dissolved in a solvent. Evaporation produces crystals, which are then used for further investigations into the material properties. However, the crystallization process is very time-consuming and highly dependent on the solution and the environmental parameters. As a result, the timing of the process is difficult to predict and very lengthy. Therefore, this paper presents a method combines two areas, computer vision and artificial intelligence, and thus offers the possibility to monitor a crystallization process. The significant points, the start and end point, are detected, and the course of the crystallization process over time is also recorded. For this purpose, a pre-trained ResNet34 network is used, which has been trained on the characteristics of crystals through transfer learning, and a visual analyzer unit for in-situ sample acquisition. With this precise measurement setup, crystallization processes can be monitored and subsequently automated. This can save time and money and accelerate research into new materials.