Ikeda, Y.; Körmann, F.; Neugebauer, J.: Impact of Interstitial Alloying of High Entropy Alloys from First Principles. TMS 2019, San Antonio, TX, USA (2019)
Hickel, T.; Zendegani, A.; Körmann, F.; Neugebauer, J.: Energetics of non-stoichiometric stacking faults in Fe–Nb alloys: An ab initio study. TMS 2019 Annual Meeting, San Antonio, TX, USA (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)
Ikeda, Y.; Körmann, F.; Neugebauer, J.: Impact of chemical compositions and interstitial alloying on the stacking fault energy of CrMnFeCoNi-based HEAs from first principles. The 2nd International Conference on High-Entropy Materials , Jeju, South Korea (2018)
Dutta, B.; Körmann, F.; Hickel, T.; Neugebauer, J.: Temperature-driven effects in functional materials: Ab initio insights. Talk at University Pierre and Marie CURIE (UPMC), Paris, France (2017)
Zendegani, A.; Körmann, F.; Hickel, T.; Hallstedt, B.; Neugebauer, J.: Thermodynamic properties of the quaternary Q phase in Al–Cu–Mg–Si: a combined ab-initio, phonon and compound energy formalism approach. International Conference on Advanced Materials Modelling (ICAMM), Rennes, France (2016)
Körmann, F.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Lattice excitations in magnetic alloys: Recent advances in ab initio modeling of coupled spin and atomic fluctuations. TMS Annual Meeting 2016, Nashville, TN, USA (2016)
Körmann, F.: Temperature dependent coupling of atomic and magnetic degrees of freedom from first-principles. Workshop on Electronic Structure Theory of Accelerated Design of Structural Materials, Moscow, Russia (2015)
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
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…