Atomistic simulation of structural and phase stability
The goal of the research group is to understand and optimise the properties of functional materials and to discover new materials by atomistic modelling and simulation. This requires adequate approaches to treat the diversity of the chemical composition (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).
In our portfolio of materials-science methods, we combine density functional theory (DFT) as accurate small-scale method, tight-binding (TB) and analytic bond-order potentials (BOPs) as approximate large-scale methods as well as structure maps as complementary data-driven method. The TB and BOP models are obtained by a coarse-graining of the electronic structure that preserves the quantum-mechanical nature of the chemical bond. The analytic BOP allow us to perform 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 that are applied in machine-learning of material properties. 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
- Analytic bond-order potentials and tight-binding
- 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
Group Members
Dr. Thomas Hammerschmidt
ICAMS
Ruhr-Universität Bochum
44780 Bochum
Germany
Tel: +49 234 32 29375
Fax: +49 234 32 14977
Email: thomas.hammerschmidt@rub.de
Recent publications
I. Pietka, R. Drautz, T. Hammerschmidt. strucscan: A lightweight Python-based framework for high-throughput material simulation Journal of Open Source Software, 7, 4719, (2022)
S. J. J. Ramakers, A. Marusczyk, M. Amsler, T. Eckl et al. Effects of thermal, elastic, and surface properties on the stability of SiC polytypes Physical Review B, American Physical Society (APS),, 106, 075201, (2022)
M. Meischein, A. Garzón-Manjón, T. Hammerschmidt, B. Xiao et al. Elemental (im-)miscibility determines phase formation of multinary nanoparticles co-sputtered in ionic liquids Nanoscale Advances, Royal Society of Chemistry (RSC),, 4, 3855–3869, (2022)
H. Kulik, T. Hammerschmidt, J. Schmidt, S. Botti et al. Roadmap on machine learning in electronic structure Electronic Structure, IOP Publishing,, 4, 023004, (2022)
T. Hammerschmidt, J. Rogal, E. Bitzek, R. Drautz. Atomic-scale modeling of superalloys Nickel base single crystals across length scales, Elsevier, 341-360, (2022)
