A FAIR research data management is of fundamental importance for new discoveries in materials science and related disciplines. This is even more so in case "FAIR" is interpreted as "Findable, and AI Ready". In order to facilitate the process to a FAIR data management across all scientific disciplines and to leverage the hidden treasures in available experimental and computational data sets,...
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials-science data. As a major improvement, we introduce a crystal-structure identification method based on Bayesian deep learning that is robust to structural noise and can treat more than 100 crystal structures. While being trained on ideal structures only, our method...
