Die DPG-Frühjahrstagung in Dresden musste abgesagt werden! Lesen Sie mehr ...

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

CPP: Fachverband Chemische Physik und Polymerphysik

CPP 18: Topical Session: Data Driven Materials Science - Materials Design II (joint session MM/CPP)

CPP 18.3: Vortrag

Montag, 16. März 2020, 12:15–12:30, BAR 205

Combining ab-initio and data-guided approaches for refractory multi-principal element alloys design — •Yury Lysogorskiy¹, Alberto Ferrari², and Ralf Drautz¹ — ¹AMS, ICAMS, Ruhr University Bochum, Bochum, Germany — ²Delft University of Technology, Delft, Netherlands

Refractory multiple principal element alloys (MPEA) nominally consist of several elements of the groups IV-VI at near-equal compositions in a single crystalline bcc phase that is characterized by exceptional high-temperature mechanical properties and a very high melting point. In this work we introduce a computationally tractable and accurate method, based on first-principles calculations and alloy modelling, to predict phase stability in MPEAs at arbitrary compositions. We reconstruct the complete phase diagram of the prototypical refractory MPEAs Mo-Nb-Ta-W and detect the regions where the formation of a solid solution is favorable at a given transition temperature. We then extend the modeling of temperature dependent properties with supervised machine learning (ML) and combine these results to a ML model for Vickers hardness, trained on experimental data from literature, to identify out-of-equiatomic composition regions of lower solid-solution formation temperature and higher hardness.