pySSpredict: A python-based solid-solution strength prediction toolkit for complex concentrated alloys

The emergence of solid solution high entropy alloys (HEAs) and complex concentrated alloys (CCAs) offers opportunities to design novel alloys with tailored strength and ductility. The growing community of integrated-computational materials engineering (ICME) can benefit from implementing state-of-th...

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Vydáno v:Computational materials science Ročník 220; s. 111977
Hlavní autoři: Wen, Dongsheng, Titus, Michael S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 05.03.2023
Témata:
Complex concentrated alloys
Dislocations
High-throughput calculations
Solid-solution strengthening
Yield strengths
Solid-solution strengthening
Yield strengths
Complex concentrated alloys
High-throughput calculations
Dislocations
ISSN:0927-0256, 1879-0801
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Shrnutí:The emergence of solid solution high entropy alloys (HEAs) and complex concentrated alloys (CCAs) offers opportunities to design novel alloys with tailored strength and ductility. The growing community of integrated-computational materials engineering (ICME) can benefit from implementing state-of-the-art solid-solution strengthening models to alloy design practices. This paper introduces pySSpredict, an open-source python-based toolkit that automates high-throughput calculations of solid-solution strengths of CCAs and thermodynamic properties. We present the functions of the pySSpredict code: (1) automating high-throughput calculations of strength for CCAs, (2) managing the data of thermodynamic calculations from databases or other software, and (3) visualizing and filtering the data to identify candidate alloys. The toolkit implements the latest theoretical edge dislocation model for face-centered cubic (FCC), and edge/screw dislocation models for body-centered cubic (BCC) alloys. The pySSpredict code is hosted on GitHub and deployed on nanoHUB for demonstrations. [Display omitted]
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2022.111977