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Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling2021 Crystals article Materials Science Machine Learning in Materials Science Open access cited by 3 patents
Patrick Trampert, Dmitri Rubinstein, Faysal Boughorbel, Christian Schlinkmann, Maria Luschkova, Philipp Slusallek, +2 more
40citations -
Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning2020 Journal of Computational Physics article Materials Science Machine Learning in Materials Science Open access cited by 3 patents
Kevin Linka, Markus Hillgärtner, Kian P. Abdolazizi, Roland Can Aydin, Mikhail Itskov, Christian Johannes Cyron
341citations -
Automatic Data Curation and Analysis Pipeline for Electrochemical Impedance Spectroscopy Measurements Conducted on Solid Oxide Cell Stacks2023 ECS Meeting Abstracts conference-abstract Materials Science Advancements in Solid Oxide Fuel Cells
Christian Mänken, Dominik Schäfer, Rüdiger‐A. Eichel, Felix Kunz
0citations -
Data-Efficient Characterization of Battery Slurry Viscosity using Uncertainty-Based Active Learning with Neural Networks2025 International Conference on Production Technologies and Systems for E-Mobility (EPTS) conference-paper Materials Science Machine Learning in Materials Science
Rui Yan Li, Christian Wittke, Robert Ludwigs, Paul Lingohr, Heiner Heimes, Oliver Niggemann, +1 more
0citations -
Research data for "Real-Space Visualization of the Intrinsic Aging in N-type Mg3(Sb, Bi)2 Thermoelectrics"2026 Zenodo (CERN European Organization for Nuclear Research) dataset Materials Science Advanced Thermoelectric Materials and Devices Open access
Yuxiang Gong, Nuo Qu, Sumayya, Yuke Zhu, Jianbo Zhu, Tinglu Song, +8 more
1citations -
Research data for "Real-Space Visualization of the Intrinsic Aging in N-type Mg3(Sb, Bi)2 Thermoelectrics"2026 Zenodo (CERN European Organization for Nuclear Research) dataset Materials Science Advanced Thermoelectric Materials and Devices Open access
Yuxiang Gong, Nuo Qu, Sumayya, Yuke Zhu, Jianbo Zhu, Tinglu Song, +8 more
1citations -
Filling the Gaps: Machine Learning Prediction of Sparse Mineral Phase Data2026 EGU General Assembly Conference Abstracts conference-abstract Materials Science Electron and X-Ray Spectroscopy Techniques Open access
Julia Schmitz, Joyce Schmatz, Mingze Jiang, Eva Wellmann, Mara Weiler, Friedrich Hawemann, +1 more
0citations -
Data and trained machine learning interatomic potentials supporting publication "Modeling the inner electric double layer using short-range machine learning potentials: A critical assessment"2026 Zenodo (CERN European Organization for Nuclear Research) dataset Materials Science Machine Learning in Materials Science Open access
Lucas B. T. de Kam, Jiaxin Zhu, Ankit Mathanker, Katharina Doblhoff-Dier, Nitish Govindarajan
0citations -
Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix2026 Zenodo (CERN European Organization for Nuclear Research) dataset Materials Science Machine Learning in Materials Science Open access
Liwei Zhang, Patrizia Mazzeo, Michele Nottoli, Edoardo Cignoni, Lorenzo Cupellini, Benjamin Stamm
1citations -
Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties2024 Machine Learning with Applications article Materials Science Machine Learning in Materials Science Open access
Binh Duong Nguyen, Pavlo Potapenko, Aytekin Demirci, Kishan Govind, Sébastien Bompas, Stefan Sandfeld
22citations -
DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions2021 Computers & Structures article Materials Science Machine Learning in Materials Science Open access
Alexander Fuchs, Yousef Heider, Kun Wang, WaiChing Sun, Michael Kaliske
58citations -
An automated approach for developing neural network interatomic potentials with FLAME2021 Computational Materials Science article Materials Science Machine Learning in Materials Science Open access
Hossein Mirhosseini, Hossein Tahmasbi, Sai Ram Kuchana, S. Alireza Ghasemi, Thomas D. Kühne
12citations