Esakkiraja, N.; Gupta, A.; Jayaram, V.; Hickel, T.; Divinski, S. V.; Paul, A.: Diffusion, defects and understanding the growth of a multicomponent interdiffusion zone between Pt-modified B2 NiAl bond coat and single crystal superalloy. Acta Materialia 195, pp. 35 - 49 (2020)
Schwarze, C.; Gupta, A.; Hickel, T.; Kamachali, R. D.: Phase-field study of ripening and rearrangement of precipitates under chemomechanical coupling. Physical Review B 95 (17), 174101 (2017)
Gupta, A.; Dutta, B.; Hickel, T.; Neugebauer, J.: Thermodynamic phase stability in the Al–Sc system using first principles methods. 2nd German-Austrian Workshop on "Computational Materials Science on Complex Energy Landscapes", Kirchdorf, Austria (2015)
Bajaj, P.; Gupta, A.; Jägle, E. A.; Raabe, D.: Precipitation kinetics during non-linear heat treatment in Laser Additive Manufacturing. International Conference on Advanced Materials and Processes, ‘ADMAT 2017’ SkyMat, Thiruvananthapuram, India (2017)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.