Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Ab Initio Calculations for electrified solid/liquid interfaces – Challenges, insights and Opportunities. GRC Aqueous Corrosion: Corrosion Challenges and Opportunities for the Energy Transition, New London, NH, USA (2024)
Freysoldt, C.; Katnagallu, S.; Neugebauer, J.; Mishra, A.; Ashton, M. W.: Perspectives for machine learning applied to data-rich experiments on complex materials. Workshop on local probes of chemical bonding and atom probe tomography at RWTH Aachen, Aachen, Germany (2024)
Neugebauer, J.; Deißenbeck, F.; Wippermann, S. M.; Todorova, M.: Getting the Electrochemical Interface into an Ab Initio Supercell. CECAM workshop "Electrochemical Interfaces in Energy Storage: Advances in Simulations, Methods and Models", Lausanne, Switzerland (2024)
Feng, S.; Gong, Y.; Neugebauer, J.; Raabe, D.; Liotti, E.; Grant, P. S.: Multi-technique investigation of Fe-rich intermetallic compounds for more impurity-tolerant Al alloys. Annual Meeting of DPG and DPG-Frühjahrstagung (DPG Spring Meeting) of the Condensed Matter Section (SKM) 2024, Berlin, Germany (2024)
Todorova, M.; Surendralal, S.; Yang, J.; Neugebauer, J.: Using ab initio calculations to unravel atomistic processes at electrified solid/ liquid interfaces. 63rd Sanibel Symposium, St. Augustine, FL, USA (2024)
Neugebauer, J.: Boosting ab initio-based materials discovery by machine learning. MPCDF Workshop “High-performance computing, artificial intelligence, and data-intensive applications in the Max-Planck Society”, Schloss Ringberg, Tegernsee, Germany (2023)
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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
Hydrogen in aluminium can cause embrittlement and critical failure. However, the behaviour of hydrogen in aluminium was not yet understood. Scientists at the Max-Planck-Institut für Eisenforschung were able to locate hydrogen inside aluminium’s microstructure and designed strategies to trap the hydrogen atoms inside the microstructure. This can…
It is very challenging to simulate electron-transfer reactions under potential control within high-level electronic structure theory, e. g. to study electrochemical and electrocatalytic reaction mechanisms. We develop a novel method to sample the canonical NVTΦ or NpTΦ ensemble at constant electrode potential in ab initio molecular dynamics…