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Home » Institute » Departments & Research Groups » Atomistic Modelling and Simulation » Atomistic Simulation of Structural and Phase Stability

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Department Atomistic Modelling and Simulation
Research Group

Atomistic Simulation of Structural and Phase Stability

The research group aims to understand and optimise the properties of functional materials and to discover new materials by atomistic modelling and simulation.


Thomas HammerschmidtRUB, Marquard
PD Dr. habil. Thomas Hammerschmidt

Research Group Leader

Room: 02-571
Tel.: +49 234 32 29375
E-Mail: thomas.hammerschmidt@rub.de




Research

It requires adequate approaches to treat the diversity of the chemical compositions (e.g. multi-component superalloys), the complexity of the microstructures (e.g. dislocations and precipitates in steels), and the complexity of the physical phenomena (e.g. magnetic phase transition in iron, finite-T properties of battery materials, dislocations in high-entropy alloys).

Three-dimensional map of structural stability of compounds formed by combining sp-valent elements with d-valent elements
Three-dimensional map of structural stability of compounds formed by combining sp-valent elements with d-valent elements. The stability regions of the different crystal structures (polyhedrons) are spanned by descriptors that are based on atomic volume (V), number of electrons/holes (N) and electro-negativity
ICAMS, RUB

In our portfolio of materials-science methods, we combine electronic-structure methods at the level of density functional theory (DFT), tight-binding (TB), and analytic bond-order potentials (BOPs) with structure maps and machine-learning as data-driven methods. The TB/BOP models are obtained by coarse-graining the electronic structure, preserving the quantum-mechanical nature of the chemical bond for large-scale atomistic simulations that capture the complexity of microstructure and physical phenomena. They also provide electronic-structure-based descriptors of the local atomic environment, which are applied in the machine-learning of material properties across chemical space. The highly predictive structure maps chart the bonding chemistry of known compounds with physically intuitive descriptors and enable us to predict structural stability in multi-component alloys.

Competences

  • Interatomic potentials based on physical models and machine learning
  • Structure maps of d-d and p-d valent systems
  • High-throughput density functional theory calculations
  • Descriptors of local atomic environments and machine learning
  • Structural stability, point defects and interfaces in transition metal compounds
Members
  • Forti, Dr. Mariano
  • Hammerschmidt, PD Dr. habil. Thomas
  • Kumar, M.Sc. Rohan
  • Vishwakarma, M. Sc. Aditya
Recent Publications
  • L. A. Ávila Calderón, Y. Shakeel, A. Gedsun et al. Management of reference data in materials science and engineering exemplified for creep data of a single-crystalline Ni-based superalloy. Acta Materialia, 286, 120735, (2025)
  • F. F Morgado, L. Stephenson, S. Bhatt et al. Stacking fault segregation imaging with analytical field ion microscopy. Microscopy and Microanalysis, 31, ozae105, (2025)
  • C. Dösinger, T. Hammerschmidt, O. Peil et al. Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies. Computational Materials Science, 247, 113493, (2025)
  • A. Egorov, A. Kraych, M. Mrovec et al. Core structure of dislocations in ordered ferromagnetic FeCo. Physical Review Materials, 8, 093604, (2024)
  • J. dos Santos, S. Griesemer, N. Dupin et al. Applying the effective bond energy formalism (EBEF) to describe the sigma (σ) phase in the Co-Cr-Ni-Re system. Journal of Phase Equilibria and Diffusion, 45, 330-357, (2024)
  • S. Kunzmann, T. Hammerschmidt, G. Schierning et al. Ab initio study of transition paths between (meta)stable phases of Nb and Ta-substituted Nb. Physical Review Materials, 8, 033603, (2024)

All publications

Research Examples

Influence of spin fluctuations on structural phase transitions of iron

Iron changes its crystal structure from α (bcc) to γ (fcc) to δ (bcc) with increasing temperature. We apply a magnetic, orthogonal, d-valent TB model to clarify the influence of spin fluctuations on these structural phase transitions.

Teaser B1
Magnetic bond-order potential for iron-cobalt alloys

We developed an analytic bond-order potential for Fe-Co alloys. We use a d-valent orthogonal tight-binding Hamiltonian in two-center approximation and employ an embedding function to account for the s electrons.

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