Dataset: Composition & predicted values (surface energy, stacking fault energy and ductility parameter) for 1184 screened alloys.

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Published: 3 years ago Views: 548 Downloads: 150 DOI: 10.13011/m3-kptn-e839 License: Open Database License (ODC-ODbL) Size: 111.46 KB
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  • Aditya Sundar
  • David Bugallo Ferron
  • Yong-Jie Hu
  • Liang Qi

Enhanced room-temperature ductility and high-temperature surface passivation are critical for the applications of body-centered cubic (BCC) refractory multicomponent alloys. Here, we devise a hierarchical workflow for the rapid initial screening of promising high-ductility single-phase compositions based on relatively simple criteria. Regression models built using data from density-functional theory calculations are used to calculate the intrinsic strength and ductility for the (1-10)[111] slip system in over 10^7 quaternary alloys, from a 13-element composition space (Ti-Zr-Hf-V-Nb-Ta-Mo-W-Re-Ru-Al-Cr-Si). Computational thermodynamics calculations are then employed to screen alloys with a single BCC phase at annealing temperatures (800°C) and surface oxidation capability at service temperatures (900°C). The corresponding manuscript has been accepted by MRS Communications (MRSC-D-22-00157R1) with the title "Automated hierarchical screening of refractory multicomponent alloys with high intrinsic ductility and surface passivation potency". Please cite our paper if you would like to use our data

A. S. and L. Q. acknowledge the financial supports from the National Science Foundation (NSF) award DMR-1847837 and Michigan Materials Research Institute (MMRI) seed fund. Y.J.H. acknowledge the financial supports from the startup fund from Drexel University. D. B. acknowledges financial support through the program for the requalification, international mobility and attraction of talent in the Spanish University system, modality Margarita Salas. The calculations were performed by using the Extreme Science and Engineering Discovery Environment (XSEDE) Stampede2 at the TACC through allocation TG-DMR190035. This research was supported in part through computational resources and services provided by Advanced Research Computing Technology Services (ARC-TS), a division of Information and Technology Services (ITS) at the University of Michigan, Ann Arbor.

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