Description
Chairs: Majid Ounsy and Zdenek Matej
Surface and interface scattering is an indispensable tool in modern thin film research and material science. X-ray and neutron reflectivity measurements offer a diverse array of experimental insights, presenting an opportunity to integrate this technique through machine learning (ML) / Neural Network-based methods. Within the framework of DAPHNE4NFDI (Data for Photon and Neutron Science),...
Researchers from Kiel and Tübingen University, as part of the DAPHNE4NFDI initiative, are collaborating to enhance machine learning models for analyzing X-ray and neutron reflectivity datasets using the Python package "mlreflect" [1] developed at Tübingen University in the group of Frank Schreiber. The collaborative effort has achieved success during beamtime at ID10 at the European...
Structures of biological macromolecules are the key to understanding the processes of life and form the basis for developing new drugs, e.g. against COVID-19. Traditionally, the initial quality of the X-ray data set is evaluated by looking at detector images as they are recorded. An expert user used to be able to recognize problems and after collection, the data would be integrated, scaled and...
Serial Synchrotron Crystallography (SSX) experiments conducted at microfocus beamlines involve the collection of diffraction data from multiple microcrystals contained within one or more experimental supports until a complete dataset is obtained (Diederichs & Wang, 2017). An experimental sample for SSX typically consists of a set of 10 to 10,000 crystals with sizes around 5x5x5 µm³. A widely...
As the field of machine learning continues to advance in photon science and related fields, the availability of high-quality datasets plays a pivotal role in the development, use, and evaluation of models. However, the landscape of datasets remains quite inaccessible and diverse, with many being unstructured, poorly organized, or lacking essential documentation. In this presentation, we...
