Data Mining and Statistical Analysis
At the moment, one of key research topics is the development of physically-based and data-driven models to identify statistically sound correlations between materials chemistry, thermodynamic, microstructure and mechanical data of single crystal Ni- and Co-based super alloys. In comparison to the existing purely phenomenological modeling methods, such hybrid modeling strategies allow to identify the influence of individual physical effects from the considered contribution on selected material properties. Moreover, they are frequently required to accelerate the computer-based design of new materials and alloys, for example, the development of rhenium-free Ni-based alloys with the desirable mechanical properties.
Due to the high complexity and heterogeneity of materials data, we focus mainly on three key goals:
- the establishment of a well-organized data infrastructure for storage of experimental data and simulation results,
- the development of robust physics-based models of materials properties and their application for thermodynamic and kinetic calculations and integration into computer-based alloy-by-design framework,
- the development and application of data mining and machine learning methods to identify statistically sound correlations between materials chemistry, microstructure and mechanical data.
Moreover, a combination of this modelling technique and the application of statistical design of experiments allow to reduce the number of experimental trials required for the development of new materials and processes.
- Data mining and machine learning
- Automation of data processing
- Physics-based models
- Third generation CALPHAD databases: mathematical aspects
- Development of the pair-diffusion model and automation of atomic mobility assessments
- Design of experiments
Dr. Irina Roslyakova
Department of Scale Bridging Thermodynamic and Kinetic Simulation
Tel: +49 234 32 22605
Bocklund, Brandon, Otis, Richard, A. Egorov, A. Obaied et al. ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu–Mg MRS Communications: Artificial Intelligence Research Letter, 9, 618-627, (2019)
D. Gaertner, K. Abrahams, J. Kottke, V. Esin et al. Concentration-dependent atomic mobilities in FCC CoCrFeMnNi high-entropy alloys Acta Materialia, 166, 357-370, (2019)
Sergeev, D., Reis, B. H., Ziegner, M., I. Roslyakova et al. Comprehensive analysis of thermodynamic properties of calcium nitrate J. Chem. Thermodynamics, 134, 187-194, (2019)
A. Müller, I. Roslyakova, M. Sprenger, P. Git et al. MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations Modelling and Simulation in Materials Science and Engineering, IOP Publishing, 27, 1-18, (2019)
A. Obaied. Application of machine learning for thermo-physical properties of transition metals Master Thesis, Ruhr-Universität Bochum (2018)