Zhu, L.-F.; Neugebauer, J.; Grabowski, B.: A computationally highly efficient ab initio approach for melting property calculations and practical applications. CALPHAD 2024, Mannheim, Germany (2024)
Zhu, L.-F.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential and pyiron workflow. New Horizons in materials design at MPIE, Düsseldorf, Germany (2023)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.