’We must learn to understand chemistry as data science’

Frank Glorius ( links ) und Philipp Pflüger erklären im Interview, was Molecular Machine Learning bedeutet. WWU - Marius Kühnemund "Molecular Machine Learning" (MML) is a new branch of research with the potential to change chemical research. Prof. Frank Glorius , coordinator of the new Priority Programme "Molecular Machine Learning" (SPP 2363), funded by the German Research Foundation (DFG), and Philipp Pflüger , who is working on his PhD in Chemistry and helped to develop the programme, explain in this interview with Christina Hoppenbrock what MML means, what opportunities and challenges this new field of research presents, and what working in chemistry will be like in tomorrow's world. What overall role does machine learning play in chemistry? Frank Glorius: Most of the issues we are faced with in chemistry, and in synthetic chemistry in particular, require the ability to recognise complex patterns. We have to recognise connections between the structures of different molecules, reactions or analytical data. Although chemists are already doing a lot, we're reaching our limits when dealing with large quantities of data - for example thousands of chemical reactions. In such cases, machine learning can help in recognising important patterns and, based on that, in drawing up generally valid models.