Materials Data Science and Informatics (IAS-9)

2023

• Iraki, T., Morand, L., Link, N., Sandfeld, S., Helm, D. Accurate Distances Measures and Machine Learning of the Texture-Property Relation for Crystallographic Textures Represented by One-Point Statistics, preprint, https://doi.org/10.48550/arXiv.2312.04214

• Govind, K., Oliveros, D., Dlouhy, A., Legros, M., Sandfeld, S. Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data", preprint, https://doi.org/10.48550/arXiv.2307.06322

• Nguyen, B. D., Potapenko, P., Demirci, A., Govind, K., Sandfeld, S. Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties, preprint, https://doi.org/10.48550/arXiv.2309.00305

• Zhang, C., Bos, C., Sandfeld, S., Schwaiger, R. Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials, preprint, https://doi.org/10.48550/arXiv.2309.06613

• Ihsan, A. Z., Fathalla, S., Sandfeld, S. Modeling Dislocation Dynamics Data Using Semantic Web Technologies, preprint, https://doi.org/10.48550/arXiv.2309.06930

• Bosoni, E., Beal, L., Bercx, M., [...], Bröder, J., [...], Austin Zadoks, A., Zhu, B., Pizzi, G. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows, Materials Cloud Archive 2023.81 (2023), doi: 10.24435/materialscloud:s4-3h.
  
• Govind, K., Oliveros, D., Dlouhy, A. Legras, M., Sandfeld, S. Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data", Preprint, https://doi.org/10.48550/arXiv.2307.06322
  
• Demirci, A., Steinberger, D., Stricker, M., Merkert, N., Weygand D., Sandfeld, S. Statistical analysis of Discrete Dislocation Dynamics simulations: initial structures, cross-slip and microstructure evolution. preprint, https://doi.org/10.48550/arXiv.2307.05767
  
• Ihsan, A. Z., Fathalla, S., Sandfeld, S. Steps towards a Dislocation Ontology for Crystalline Materials. SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 1746–1753 (2023), https://doi.org/10.1145/3555776.3578739

2022

• Jejkal, T., Pfeil, A., Schweikert, J., Pirogov, A., Videgain Barranco, P., Krebs, F., Koch, C., Guenther, G, Curdt, C., Weinelt, M. Realizin FAIR Digital Objects for the German Helmholtz Association of Research Centres, First International Conference on FAIR Digital Objects (2022) https://doi.org/10.3897/rio.8.e94758
• Buttigieg, P. L., Curdt, C., Ihsan, A.Z., Jejkal, T., Kubin, M., Mannix, O., Mohr, D. P., Pirogov, A., Port, B. and Stucky, K.-U. and Helmholtz Metadata Collaboration (HMC) (2022) An interpretation of the FAIR principles to guide implementations in the HMC digital ecosystem. HMC Paper. HMC-Office, GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany, 26 pp. DOI 10.3289/HMC_publ_01.
• A. Pirogov, F. D'Mello, V. Hofmann, S. Sandfeld. Metadata in the Research Workflow: Tools for Enrichment and Validation of Structured Metadata, Poster, Helmholtz Metadata Collaboration | Conference 2022, online, 5-6 October 2022, https://doi.org/10.5281/zenodo.7221235

2021

• Bröder, J. ; Hofmann, V. ; Wortmann, D. ; Blügel, S. ; Sandfeld, S. Making computational materials science data FAIR. SKM 2021, Virtual, Germany, 27 Sep 2021 - 1 Oct 2021
• Ihsan, A. Z., Dessì, D., Alam, M., Sack, H., Sandfeld, S. Steps towards a Dislocation Ontology for Crystalline Materials. arXiv preprint arXiv:2106.15136, Proceedings of the Second International Workshop on Semantic Digital Twins (2021)