Jakob’s work focuses on understanding the physical chemistry of nanocrystal synthesis in lead halide and gold nanocrystals. While it is possible to make different sizes and shapes nanocrystals, scientists have only a rough, qualitative understanding of the chemical processes. Jakob uses in-situ observations in the Alivisatos lab and high-throughput experiments in the Chan lab to generate 1000s of datapoints on nanocrystal synthesis. Machine learning techniques help analyze this data. Currently, he is working with the Limmer and Persson groups to develop chemistry-based machine learning models that will quantify and clarify our understanding of the complex reaction networks involved in forming nanocrystals. The goal is to enable robust, chemical-model based predictions for nanocrystal yield, size, shape and composition for synthesis procedures that have not previously been attempted. Ideally, this will both speed up nanocrystal synthesis exploration and lead to enhanced understanding of the underlying chemical processes.