Zhu, L.-F.; Neugebauer, J.; Grabowski, B.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. CALPHAD L Conference, Cambridge, MA, USA (2023)
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)
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.; Todorova, M.; Grabowski, B.; Hickel, T.: Modelling structural materials in realistic environments by ab initio thermodynamics. Hume-Rothery Award Symposium, TMS2019 Annual Meeting and Exhibition, 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)
Zhu, L.-F.; Grabowski, B.; Neugebauer, J.: Efficient approach to compute melting properties fully from ab initio with application to Cu. CALPHAD XLVII Conference, Querétaro, México (2018)
Grabowski, B.: Knowledge driven engineering of materials: Development and application of ab initio based scale bridging methods. Seminar at HSU Hamburg, Hamburg, Germany (2018)
Grabowski, B.: Efficient and Accurate Computation of Melting Temperatures and Enthalpies and Entropies of Fusion from Ab Initio. TMS conference, Phoenix, AZ, USA (2018)
Grabowski, B.: Knowledge driven engineering of materials: Development and application of ab initio based scale bridging methods. Seminar at University Stuttgart, Stuttgart, Germany (2017)
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
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…
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 field of micromechanics has seen a large progress in the past two decades, enabled by the development of instrumented nanoindentation. Consequently, diverse methodologies have been tested to extract fundamental properties of materials related to their plastic and elastic behaviour and fracture toughness. Established experimental protocols are…
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…
Electron microscopes offer unique capabilities to probe materials with extremely high spatial resolution. Recent advancements in in situ platforms and electron detectors have opened novel pathways to explore local properties and the dynamic behaviour of materials.