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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| Published in: | Computational materials science Vol. 220; p. 111977 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
05.03.2023
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| ISSN: | 0927-0256, 1879-0801 |
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| Abstract | 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.
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| AbstractList | 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.
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| ArticleNumber | 111977 |
| Author | Titus, Michael S. Wen, Dongsheng |
| Author_xml | – sequence: 1 givenname: Dongsheng orcidid: 0000-0002-0728-0231 surname: Wen fullname: Wen, Dongsheng email: wen94@purdue.edu – sequence: 2 givenname: Michael S. orcidid: 0000-0002-3423-4505 surname: Titus fullname: Titus, Michael S. |
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| Keywords | Solid-solution strengthening Yield strengths Complex concentrated alloys High-throughput calculations Dislocations |
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