E-Mail A team headed by Dr. Phillip M. Maffettone (currently at National Synchrotron Light Source II in Upton, USA) and Professor Andrew Cooper from the Department of Chemistry and Materials Innovation Factory at the University of Liverpool joined forces with the Bochum-based group headed by Lars Banko and Professor Alfred Ludwig from the Chair of Materials Discovery and Interfaces and Yury Lysogorskiy from the Interdisciplinary Centre for Advanced Materials Simulation. The international team published their report in the journal Nature Computational Science from 19 April 2021. Previously manual, time-consuming, error-prone Efficient analysis of X-ray diffraction data (XRD) plays a crucial role in the discovery of new materials, for example for the energy systems of the future. It is used to analyse the crystal structures of new materials in order to find out, for which applications they might be suitable. XRD measurements have already been significantly accelerated in recent years through automation and provide large amounts of data when measuring material libraries. "However, XRD analysis techniques are still largely manual, time-consuming, error-prone and not scalable," says Alfred Ludwig. "In order to discover and optimise new materials faster in the future using autonomous high-throughput experiments, new methods are required."