Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

1 code implementation • 21 Mar 2023 • Henrik Schopmans, Patrick Reiser, Pascal Friederich

However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.

Graph neural networks for materials science and chemistry

no code implementations • 5 Aug 2022 • Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e. g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials.

Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis

1 code implementation • 31 Jul 2019 • Tobias A. de Jong, David N. L. Kok, Alexander J. H. van der Torren, Henrik Schopmans, Rudolf M. Tromp, Sense Jan van der Molen, Johannes Jobst

Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively.

Materials Science Instrumentation and Detectors