The past few years have witnessed booming research in machine learning in chemistry and materials sciences. New pharmaceutical molecules and new energy materials have been identified by machine learning, leading to a paradigm shift in research and industry. Quantum materials, on the other hand, despite constant new reports in using machine learning, have experienced significant challenge due...
Very recently, it became possible to combine propagation-based phase contrast-imaging (PCI) and X-ray diffraction at extreme conditions at the Extreme Conditions Beamline (P02.2), PETRA III, DESY, Hamburg. This first platform for such experiments enables the investigation of hierarchical structures at conditions approaching those observed in the internal structure of planets, with pressures...
The position that ions occupy in the unit cell of a crystal and in the periodic table of elements, fully determines the physical, chemical and functional properties of materials. Through diffraction experiments, such as X-ray and neutron scattering, it is possible to determine the crystal structure of a material. However, when such experiments are difficult to conduct (e.g. requiring...
Neutron scattering is a versatile and powerful technique widely used in materials science to gain insights into materials' properties and uncover new materials. However, this method is often expensive and time-consuming, requiring advanced detector technology and complex data reduction and analysis procedures. Machine learning (ML) has opened new avenues for neutron diffraction data reduction...
During this talk, I will discuss our work [1] to use neural networks to automatically classifiy Bravais lattices and space-groups from
neutron powder diffraction data. Our work classifies 14 Bravais lattices and 144 space groups. The novelty of our approach is to use semi- supervised and self-supervised learning to allow for training on data sets with unlabeled data as is common at user...
