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Home » Institute » Departments & Research Groups » Atomistic Modelling and Simulation » Atomistic Simulation of Mechanical Behaviour

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Department Atomistic Modelling and Simulation
Research Group

Atomistic Simulation of Mechanical Behaviour

The group’s primary goal is to understand phenomena occurring on the atomic scale that are related to macroscopic mechanical behaviour.


Matous MrovecRUB, Marquard
Dr. Matous Mrovec

Research Group Leader

Room: 02-571
Tel.: +49 234 32 29313
E-Mail: matous.mrovec@icams.rub.de




Research

We start with modelling intrinsic material properties related to chemical bonding but eventually concentrate on the role of crystal imperfections. The imperfections encompass fundamental crystal defects such as vacancies, dislocations, and grain boundaries in single-component crystalline materials as well as complex microstructural features such as semicoherent interfaces, precipitates, and secondary phases that constitute the microstructure of technologically important multi-phase and multi-component systems.

Visualisations of the training dataset in terms of energy per atom with respect to the nearest interatomic distance within each structure (left) and the distribution of cohesive energies (right).
ICAMS, RUB

We are interested in materials with prototypical metallic and covalent chemical bonding as well as in those with mixed metallic-covalent or covalent-ionic character such as transition metals and their compounds, perovskite oxides, and carbon-based materials. The methods and models we employ span the whole atomistic modelling hierarchy from accurate first-principles methods through approximate electronic structure approaches to novel interatomic potentials. Recently, we have focused on the development and application of atomic cluster expansion (ACE) models that can reach the accuracy and transferability of electronic structure methods while remaining highly computationally efficient and applicable in large-scale atomistic simulations. We also integrate atomistic simulations and mesoscale techniques (DDD, kMC), phenomenological and continuum theories as well as experiments.

Competences

  • Interatomic potentials
  • Transition metals and their compounds
  • Crystal defects and imperfections
  • Hydrogen embrittlement
  • Magnetism
Members
  • Bizot, Dr. Quentin
  • Fedorov, M. Sc. Evgenii
  • Mrovec, Dr. Matous
  • Qamar, M.Sc. Minaam
  • Smirnova, Dr. Daria
  • Starikov, Dr. Sergei
Recent Publications
  • B. Bienvenu, M. Todorova, J. Neugebauer et al. Development of an atomic cluster expansion potential for iron and its oxides. npj Computational Materials, 11, 81, (2025)
  • S. Starikov. Dislocation mobility function as a key to understanding plasticity of refractory metals and alloys. Computational Materials Science, 246, 113411, (2025)
  • S. Menon, Y. Lysogorskiy, A. Knoll et al. From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows. npj Computational Materials, 10, 261, (2024)
  • S. V. Sevlikar, G. M. Muralikrishna, D. Gaertner et al. Grain boundary diffusion and segregation of Cr in Ni S11(113)[110] bicrystals: Decoding the role of grain boundary defects. Acta Materialia, 278, 120229, (2024)
  • A. Egorov, A. Kraych, M. Mrovec et al. Core structure of dislocations in ordered ferromagnetic FeCo. Physical Review Materials, 8, 093604, (2024)
  • S. Starikov, P. Grigorev, R. Drautz et al. Large-scale atomistic simulation of diffusion in refractory metals and alloys. Physical Review Materials, 8, 043603, (2024)

All publications

Research Examples

Angular-dependent interatomic potential for large-scale atomistic simulation of the Fe-Cr-H ternary system

The recently developed angular-dependent potential for pure iron was advanced to the interatomic potential of the Fe-Cr-H ternary system. The new potential allows to simulate Fe-Cr alloys for a wide range of compositions and different concentrations of hydrogen.

Teaser B1
Atomic Cluster Expansion (ACE) for carbon

We have recently developed an atomic cluster expansion (ACE) for carbon, which presents a significant improvement over available classical and machine learning potentials..

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