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

J. Janssen, S. Surendralal, Y. Lysogorskiy, M. Todorova et al. pyiron: An integrated development environment for computational materials science Computational Materials Science, 163, 24-36, (2019)

A. Ferrari, P. Kadletz, T. Chakraborty, K.-Y. Liao et al. Reconciling experimental and theoretical data in the structural analysis of Ti-Ta shape memory alloys Shape Memory and Superelasticity, 5, 3, (2019)

T. Hammerschmidt, B. Seiser, M. Ford, A. N. C. Ladines et al. BOPfox program for tight-binding and analytic bond-order potential calculations Computer Physics Communications, 235, 221-233, (2019)

Y. Lysogorskiy, T. Hammerschmidt, J. Janssen, J. Neugebauer et al. Transferability of interatomic potentials for molybdenum and silicon Modelling and Simulation in Materials Science and Engineering, 27, 025007, (2019)

A. G. Kiiamov, Y. Lysogorskiy, F. G. Vagizov, L. R. Tagirov et al. Vibrational properties and magnetic specific heat of the covalent chain antiferromagnet RbFeSe2 Physical Review B, 98, 214411, (2018)

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