Synthesis planning is a cornerstone of chemical research and its industrial application. Two aspects play an important role: time and selectivity. On the one hand, it is necessary that the conversion of reactants to desired products occurs within an economically viable period of time. On the other hand, the formation of unwanted byproducts is to be prevented. We are concerned with the design of tailored electrophiles and nucleophiles, which undergo selective reactions on well-defined time scales. For this purpose, we develop and study methods of machine learning and quantum chemistry.
Machine learning is usually associated with large data sets, which are readily available for pioneering applications such as image recognition, spam filters, etc. In the sciences, however, data generation is expensive, difficult, and time-consuming. Therefore, it is desirable to build powerful prediction models with as few data points as possible. To realize such a data-economic scenario, we develop and study active-learning algorithms. We reached an important milestone with the release of the batchwise variance-based sampling (BVS) method developed in collaboration with the group of Markus Reiher (ETH Zurich).