ICAMS / Interdisciplinary Centre for Advanced Materials Simulation

Data-driven methods for atomistic simulations

Yury Lysogorskiy

The research group is working on the application of data-driven methods in materials science with a particular focus on the atomic scale. A major research area is the automated extraction, analysis and validation of models in materials science, with specific application to interatomic potentials. We perform high-throughput calculations of materials properties at different levels of theory, including both density functional theory and effective interatomic potentials using the pyiron computational framework, which we co-develop with the Computational Materials Design department at the Max Planck Institute for iron research

Computational and data infrastructure for interatomic potentials validation. (Click image to enlarge.)

Another research area is the application of machine learning methods to large data sets, for example, from combinatorial or high-throughput methods and to provide efficient and supporting tools for materials discovery.

Pair plots of formation energies and band gaps of Al-Ga-In sesquioxides as calculated by density functional theory and predicted by surrogate machine learning models. (Click image to enlarge.)

 

Competences

  • data-driven methods (machine learning) in materials science
  • high-throughput calculations (DFT and molecular dynamics)
  • interatomic potentials and their validation
  • data management & visualization
Contact

Dr. Yury Lysogorskiy
ICAMS
Ruhr-Universität Bochum
44780 Bochum
Germany
Tel: +49 234 32 29300
Fax: +49 234 32 14977

Email: yury.lysogorskiy@icams.rub.de

Group Members

 

Recent publications

S. Starikov, D. Smirnova, T. Pradhan, Y. Lysogorskiy et al. Angular-dependent interatomic potential for large-scale atomistic simulation of iron: Development and comprehensive comparison with existing interatomic models Physical Review Materials, 5, 063607, (2021)

Y. Lysogorskiy, C. van der Oord, A. Bochkarev, S. Menon et al. Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon npj Computational Materials, 7, 1-12, (2021)

A. Ferrari, Y. Lysogorskiy, R. Drautz. Design of refractory compositionally complex alloys with optimal mechanical properties Physical Review Materials, 5, 063606, (2021)

P. Maffettone, L. Banko, P. Cui, Y. Lysogorskiy et al. Crystallography companion agent for high-throughput materials discovery Nature Computational Science, 1, 290-297, (2021)

S. Starikov, I. Gordeev, Y. Lysogorskiy, L. Kolotova et al. Optimized interatomic potential for study of structure and phase transitions in Si-Au and Si-Al systems Computational Materials Science, 184, 109891, (2020)

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