Python-based Toolkit for Solid Solution Strengthening Prediction

By Dongsheng Wen1; Michael S Titus1

1. Purdue University

A python-based tool for prediction solid solution strengthening of complex concentrated alloys using the state-of-the-art models.

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Version 1.1 - published on 25 Apr 2022

doi:10.21981/FCNK-8170 cite this

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    Coupling phase diagram with solid-solution strengthening predictions. TiNbZr predicted strengths vs. temperature (Suzuki model).

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Tools

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Abstract

pySSpredict is a Python-based tool for Solid-solution Strengthening prediction for complex-concentrated alloys. It can be easily installed on the high-throughput computation resources and integrated with TC-Python for designing high-temperature high-strength structural materials. 

Four different models are implemented for CCAs:

For face-centered cubic alloys: 

[Labusch model for edge dislocations]  

For body-centered cubic alloys: 

[Labusch model for edge dislocations]

[Suzuki model for screw dislocations]

[Curtin model for screw dislocations]

The source code is hosted on GitHub [repository]. 

References

Varvenne, C., Leyson, G.P.M., Ghazisaeidi, M. and Curtin, W.A., 2017. Solute strengthening in random alloys. Acta Materialia, 124, pp.660-683.

Maresca, F. and Curtin, W.A., 2020. Mechanistic origin of high strength in refractory BCC high entropy alloys up to 1900K. Acta Materialia, 182, pp.235-249.

Maresca, F. and Curtin, W.A., 2020. Theory of screw dislocation strengthening in random BCC alloys from dilute to “High-Entropy” alloys. Acta Materialia, 182, pp.144-162.

Rao, S.I., Woodward, C., Akdim, B., Senkov, O.N. and Miracle, D., 2021. Theory of solid solution strengthening of BCC Chemically Complex Alloys. Acta Materialia, 209, p.116758.

 

Publications

Wen, D., Chang, C.H., Matsunaga, S., Park, G., Ecker, L., Gill, S.K., Topsakal, M., Okuniewski, M.A., Antonov, S., Johnson, D.R. and Titus, M.S., 2020. Structure and tensile properties of Mx (MnFeCoNi) 100-x solid solution strengthened high entropy alloys. Materialia, 9, p.100539.

Cite this work

Researchers should cite this work as follows:

  • Dongsheng Wen, Michael S. Titus, (2021), "Python-based Toolkit for Solid Solution Strengthening Prediction," https://nanohub.org/resources/pysspredict. 

    Wen, D., Chang, C.H., Matsunaga, S., Park, G., Ecker, L., Gill, S.K., Topsakal, M., Okuniewski, M.A., Antonov, S., Johnson, D.R. and Titus, M.S., 2020. Structure and tensile properties of Mx (MnFeCoNi) 100-x solid solution strengthened high entropy alloys. Materialia, 9, p.100539.

     

  • Dongsheng Wen, Michael S Titus (2022), "Python-based Toolkit for Solid Solution Strengthening Prediction," https://nanohub.org/resources/pysspredict. (DOI: 10.21981/FCNK-8170).

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Tags

  1. complex concentrated alloys
  2. Dislocation Engineering
  3. high entropy alloys
  4. High Throughput Computing
  5. stress