Neugebauer, J.: Boosting ab initio-based materials discovery by machine learning. AI MSE 2023 - Artificial Inteligence in Materials Science and Engineering, Saarbrücken, Germany (2023)
Neugebauer, J.; Körmann, F.; Ferrari, A.: Navigating and exploiting the high-dimensional configuration spaces of high entropy alloys. The 11th International Conference on Multiscale Materials Modeling, Prague, Czech Republic (2023)
Neugebauer, J.: Capturing the chemical complexity of HEAs by ab initio based modelling. CHEAC Summer School 2023 - High Entropy Materials and their properties, Metalskolen-Jørlunde, Denmark (2023)
Gong, Y.; Ikeda, Y.; Körmann, F.; Neugebauer, J.: Ab initio computation of phase stability and interstitial alloying in bcc compositionally complex alloys. International Conference on High-Entropy Materials (ICHEM 2023), Knoxville, TN, USA (2023)
Zhu, L.-F.; Neugebauer, J.; Grabowski, B.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. CALPHAD L Conference, Cambridge, MA, USA (2023)
Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Insights into Electrified Solid/Liquid Interfaces from Ab initio and Atomistic Molecular Dynamics Simulations. CECAM - Young Researchers' School on Theory and Simulation in Electrochemical Conversion Processes, Paris, France (2023)
Neugebauer, J.: Current problems in Materials Sciences. New Mathematics for the Exascale: Applications to Materials Science Tutorials, Los Angeles, CA, USA (2023)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
International research team shows how hydrogen affects Nickel-base superalloys at elevated temperatures. Latest results published in journal Nature Materials.