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)
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)
Zhu, L.-F.; Janßen, J.; Grabowski, B.; Neugebauer, J.: Melting parameters from ab initio using the fast statistical sampling TOR-TILD approach: Applications to Al and Ni. CALPHAD XLVIII CONFERENCE, Singapore, Singapore (2019)
Neugebauer, J.; Janßen, J.; Körmann, F.; Hickel, T.; Grabowski, B.: Exploration of large ab initio data spaces to design materials with superior mechanical properties. Physics and Theoretical Division Colloquium, Los Alamos, NM, USA (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.