Nascimento, A.; Roongta, S.; Diehl, M.; Beyerlein, I. J.: A machine learning model to predict yield surfaces from crystal plasticity simulations. International Journal of Plasticity 161, 103507 (2023)
Otto de Mentock, D.; Roongta, S.; Shanthraj, P.; Eisenlohr, P.; Diehl, M.; Roters, F.: Challenges of Developing and Scaling up DAMASK, a Unified Large-strain Multi-physics Crystal Plasticity Simulation Software. TMS - Algorithm Development in Materials Science and Engineering, Orlando, FL, USA (2024)
Roters, F.; do Nascimento, A. W. P.; Roongta, S.; Diehl, M.: An optimized method for the simulation-based determination of initial parameters of advanced yield surfaces for sheet metal forming applications. Complas 2021, online (2021)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
In this project, we employ atomistic computer simulations to study grain boundaries. Primarily, molecular dynamics simulations are used to explore their energetics and mobility in Cu- and Al-based systems in close collaboration with experimental works in the GB-CORRELATE project.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.
In this ongoing project, we investigate spinodal fluctuations at crystal defects such as grain boundaries and dislocations in Fe-Mn alloys using atom probe tomography, electron microscopy and thermodynamic modeling [1,2].