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Home » Institute » Departments & Research Groups » Materials Informatics and Data Science

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ICAMS Research Group

Materials Informatics and Data Science

Key elements of the group’s work are the application of statistical and machine learning methods as well as data science approaches to materials science problems, mostly based on simulated data. With this focus, the research group strengthens and extends the “informatics” activities at ICAMS.


Markus StrickerRUB, Marquard
Prof. Dr. Markus Stricker

Professor

Room: 02-503
Tel.: +49 151 2611 4982
E-Mail: markus.stricker@rub.de




Research

Applications of these methods focus on bridging between simulation methods like atom-continuum or mesoscale-continuum and connecting knowledge from simulations to experiments.

Bridging the scale: Atoms, microstructures, properties.
Snapshot from a nudged elastic band (NEB) simulation of the basal cross-slip process in pure Magnesium calculated with a Behler-Parrinello neural network interatomic potential. Colors indicate crystallographic arrangement with green: face-centred cubic; blue: body-centred cubic; grey: others; hexagonally close-packed atoms are not shown.
ICAMS, RUB

Current materials science research questions are related to plasticity of crystalline materials. In the past and present, this has included mechanical aspects of tribological contacts, strain hardening and dislocation multiplication, dislocation grain boundary interaction, development of interatomic potentials to assess energetics and dynamics of crystalline defects on the atomic level, and data fusion from simulation and experiment to understand and quantify observed material behavior from simulations and experiments. Research questions on the methodical side revolve around appropriate data formats, standardization and representation of information.



Competences

  • Plasticity modeling with mesoscale simulations and scale bridging
  • Validation of interatomic potentials based on machine-learning methods
  • Simulation of atomistic processes
  • Data fusion from simulations and experiments

MIDS group photo, November 2024.
MIDS group photo, November 2024.
ICAMS, RUB
Members
  • Arslan, Yagiz
  • Baghaee Ravari, M. Sc. Sepideh
  • Lathiya, M.Sc. Gauravkumar
  • Mahdavi Jafari, B.Sc. Mehrdad
  • Mardani, Behnam
  • Mohamed, Doaa
  • Pemma, M.Sc. Swetha
  • Stricker, Prof. Dr. Markus
  • Udofia, Benjamin
  • Yansori, Niloofar
  • Zhang, Qi
  • Zhang, M.Sc. Lei
Recent Publications
  • S. Teng, A. Dimou, B. Udofia et al. Control of ferroelectric domain wall dynamics by point defects: Insights from ab initio based simulations. Journal of Applied Physics, 137, 154103, (2025)
  • B. Udofia, T. Jogi, M. Stricker. Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints. APL Machine Learning, 3, 016103, (2025)
  • A. Chauniyal, P. Thome, M. Stricker. Employing constrained nonnegative matrix factorization for microstructure segmentation. Microscopy and Microanalysis, 30, 712–723, (2024)
  • M. Stricker, D. Weygand. A model for physical dislocation transmission through grain boundaries and its implementation in a discrete dislocation dynamics tool. Journal of Materials Science: Materials Theory, 8, 12, (2024)
  • L. Zhang, M. Stricker. MatNexus: A comprehensive text mining and analysis suite for materials discovery. SoftwareX, 26, 101654, (2024)
  • R. Khachaturyan, Y. Yang, S. Teng et al. Microscopic insights on field induced switching and domain wall motion in orthorhombic ferroelectrics. Physical Review Materials, 8, 024403, (2024)

All publications



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