Todorova, M.; Yoo, S.-H.; Surendralal, S.; Neugebauer, J.: Predicting atomic structure and chemical reactions at solid-liquid interfaces by first principles. Operando surface science – Atomistic insights into electrified solid/liquid interfaces (708. WE-Heraeus-Seminar), Physikzentrum, Bad Honnef, Germany (2019)
Neugebauer, J.: Machine Learning in Materials: Screening and Discovery. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan (2019)
Ikeda, Y.; Ishibashi, S.; Neugebauer, J.; Körmann, F.: Tuning stacking-fault energies and local lattice distortions in high-entropy alloys. Theory of Complex Disorder in Materials (TCDM2019) , Linköping, Sweden (2019)
Neugebauer, J.; Surendralal, S.; Todorova, M.: First-principles appraoch to model electrochemical reactions at solid-liquid interfaces. ACS 2019 Fall Meeting & Exhibition, San Diego, CA, USA (2019)
Todorova, M.; Surendralal, S.; Neugebauer, J.: Degradation processes at surfaces and interfaces. ISAM4: The fourth International Symposium on Atomistic and Multiscale Modeling of Mechanics and Multiphysics, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany (2019)
Neugebauer, J.; Huber, L.; Körmann, F.; Grabowski, B.; Hickel, T.: Ab initio input for multiphysics models: Accuracy, performance and challenges. ISAM4: The fourth International Symposium on Atomistic and Multiscale Modeling of Mechanics and Multiphysics, Erlangen, Germany (2019)
Neugebauer, J.: Machine Learning in Materials: Screening and Discovery. Gordon Research Conference Physical Metallurgy „Coupling Computation, Data Science and Experiments in Physical Metallurgy“, Manchester, NH, USA (2019)
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
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…
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…
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…
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…