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)
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
This project will aim at developing MEMS based nanoforce sensors with capacitive sensing capabilities. The nanoforce sensors will be further incorporated with in situ SEM and TEM small scale testing systems, for allowing simultaneous visualization of the deformation process during mechanical tests
The utilization of Kelvin Probe (KP) techniques for spatially resolved high sensitivity measurement of hydrogen has been a major break-through for our work on hydrogen in materials. A relatively straight forward approach was hydrogen mapping for supporting research on hydrogen embrittlement that was successfully applied on different materials, and…
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…
Photovoltaic materials have seen rapid development in the past decades, propelling the global transition towards a sustainable and CO2-free economy. Storing the day-time energy for night-time usage has become a major challenge to integrate sizeable solar farms into the electrical grid. Developing technologies to convert solar energy directly into…
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…