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pyMPEALab Toolkit for Accelerating Phase Design in Multi-principal Element Alloys
Upadesh Subedi,Anil Kunwar,Yuri Amorim Coutinho,Khem Gyanwali 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.1
Multi-principal element alloys (MPEAs) occur at or nearby the centre of the multicomponent phase space, and they have theunique potential to be tailored with a blend of several desirable properties for the development of materials of future. The lackof universal phase diagrams for MPEAs has been a major challenge in the accelerated design of products with these materials. This study aims to solve this issue by employing data-driven approaches in phase prediction. A MPEA is frst representedby numerical fngerprints (composition, atomic size diference , electronegativity , enthalpy of mixing , entropy of mixing, dimensionless Ω parameter, valence electron concentration and phase types ), and an artifcial neural network (ANN) isdeveloped upon the datasets of these numerical descriptors. A pyMPEALab GUI interface is developed on the top of thisANN model with a computational capability to associate composition features with remaining other input features. With theGUI interface, an user can predict the phase(s) of a MPEA by entering solely the information of composition. It is furtherexplored on how the knowledge of phase(s) prediction in composition-varied AlxCrCoFeMnNi and CoCrNiNbx can help inunderstanding the mechanical behavior of these MPEAs.