Todorova, M.; Yoo, S.-H.; Surendralal, S.; Neugebauer, J.: Insights into the stability and reactivity of solid/liquid interfaces from ab initio calculations. 71st Annual Meeting of the International Society of Electrochemistry "Electrochemistry towards Excellence", virtual, Belgrade, Serbia (2020)
Neugebauer, J.; Lymperakis, L.; Janßen, J.; Huber, L.; Hickel, T.: Modeling crystal growth and materials design in high dimensional chemical and structural configuration spaces. German Conference on Crystal Growth DKT 2020, München/Garching, Germany (2020)
Hickel, T.; Aydin, U.; Sözen, H. I.; Dutta, B.; Pei, Z.; Neugebauer, J.: Innovative concepts in materials design to boost renewable energies. Seminar of Institute for Innovative Technologies, SRH Berlin University of Applied Sciences, Berlin, Germany (2020)
Janßen, J.; Hickel, T.; Neugebauer, J.: Automated ab-initio Determination of Materials Properties at finite Temperatures with pyiron. CNLS Seminar, Los Alamos, NM, USA (2019)
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)
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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…