Metadata Schema to support FAIR Data in Scanning Electron Microscopy. - Reetu Joseph; Aditya Chauhan; Catriona Eschke; Ahmad Zainul Ihsan; Mehrdad Jalali; Ute Jäntsch; Nicole Jung; C. N. Shyam Kumar; Christian Kübel; Christian Lucas; Matthias Mail; Andrey Mazilkin; et al. (DAMDID/RCDL (Supplementary Proceedings), 2021)

Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis. - M. Vimal; Stefan Sandfeld; A. Prakash (CoRR, 2021)

OptiMic: A tool to generate optimized polycrystalline microstructures for materials simulations. - Prince Henry Serrao; Stefan Sandfeld; Aruna Prakash (SoftwareX, 2021)

Automated analysis of continuum fields from atomistic simulations using statistical machine learning. - Aruna Prakash; Stefan Sandfeld (CoRR, 2022)

Knowledge Graph Based RDM Solutions NFDI4Culture - NFDI-MatWerk - NFDI4DataScience. - Harald Sack; Torsten Schrade; Oleksandra Bruns; Etienne Posthumus; Tabea Tietz; Ebrahim Norouzi; Jörg Waitelonis; Heike Fliegl; Linnaea Söhn; Julia Tolksdorf; Jonatan Jalle Steller; Abril Azócar Guzmán; et al. (CoRDI, 2023)

DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials. - Ahmad Zainul Ihsan; Said Fathalla; Stefan Sandfeld (SAC, 2023)

Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties. - Binh Duong Nguyen; Pavlo Potapenko; Aytekin Dermici; Kishan Govind; Stefan Sandfeld (CoRR, 2023)

Instance Segmentation of Dislocations in TEM Images. - Karina Ruzaeva; Kishan Govind; Marc Legros; Stefan Sandfeld (CoRR, 2023)

Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials. - Chen Zhang; Clémence Bos; Stefan Sandfeld; Ruth Schwaiger (CoRR, 2023)

A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables. - Sébastien Bompas; Stefan Sandfeld (CoRR, 2023)

Machine learning for structure-guided materials and process design. - Lukas Morand; Tarek Iraki; Johannes Dornheim; Stefan Sandfeld; Norbert Link; Dirk Helm (CoRR, 2023)

Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy. - Bashir Kazimi; Karina Ruzaeva; Stefan Sandfeld (CVPR Workshops, 2024)

The Helmholtz Knowledge Graph: Driving the Transition Towards a FAIR Data Ecosystem in the Helmholtz Association. - Jens Bröder; Gabriel Preuß; Fiona D'Mello; Said Fathalla; Volker Hofmann; Stefan Sandfeld (ESWC Satellite Events (1), 2024)

The Helmholtz Digitization Ontology: Representing Digital Assets in the Helmholtz Digital Ecosystem. - Said Fathalla; Gerrit Günther; Leon Steinmeier; Christine Lemster; Dorothee Kottmeier; Lakxmi Sivapatham; Pier Luigi Buttigieg; Volker Hofmann; Stefan Sandfeld (SEMANTiCS (Posters, Demos, Workshops & Tutorials), 2024)

Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer. - Binh Duong Nguyen; Johannes Steiner; Peter Wellmann; Stefan Sandfeld (CoRR, 2024)

Self-Supervised Learning in Electron Microscopy: Towards a Foundation Model for Advanced Image Analysis. - Bashir Kazimi; Karina Ruzaeva; Stefan Sandfeld (CoRR, 2024)

Deep learning of crystalline defects from TEM images: a solution for the problem of 'never enough training data'. - Kishan Govind; Daniela Oliveros; Antonin Dlouhy; Marc Legros; Stefan Sandfeld (Mach. Learn. Sci. Technol., 2024)

Modeling dislocation dynamics data using semantic web technologies. - Ahmad Zainul Ihsan; Said Fathalla; Stefan Sandfeld (Neural Comput. Appl., 2025)