Janßen, J.; Hickel, T.; Neugebauer, J.: pyiron – an integrated development environment for ab initio thermodynamics. AMS Seminar, virtual, Bochum, Germany (2020)
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)
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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…