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)
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 develop more efficient catalysts for energy conversion, the relationship between the surface composition of MXene-based electrode materials and its behavior has to be understood in operando. Our group will demonstrate how APT combined with scanning photoemission electron microscopy can advance the understanding of complex relationships…