Data

Tools

Indexes

Applications

Experiments

Open Source Science

pysspredict

pySSpredict demo on nanoHUB: https://nanohub.org/tools/pysspredict

https://github.com/dongsheng-wen/pysspredict

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 22 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (5.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

pySSpredict demo on nanoHUB: https://nanohub.org/tools/pysspredict

Basic Info
  • Host: GitHub
  • Owner: Dongsheng-Wen
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 31.4 MB
Statistics
  • Stars: 9
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

pySSpredict

Description

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 desiging high-temperature high-strength structural materials. Contact email: wen-dongsheng@alumni.purdue.edu.

Publications

  1. Wen, D., Titus, M.S., 2023. pySSpredict: A python-based solid-solution strength prediction toolkit for complex concentrated alloys. Computational Materials Science.

  2. 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.

Models

Solid solution strengthening models:
FCC edge dislocation-solute interaction C. Varvenne, G.P.M. Leyson, M. Ghazisaeidi, W.A. Curtin (2017)
BCC edge dislocation-solute interaction F. Maresca, W.A. Curtin (2019)
BCC screw dislocation-solute interaction F. Maresca, W.A. Curtin (2019)
BCC screw dislocation-solute Suzuki model S.I. Rao, C. Woodward, B. Akdim, O.N. Senkov, D. Miracle (2021)

Examples

Jupyter notebooks:
Simple calculations and plots
FCC_edge
BCC_edge
BCC_screw

FCC edge dislocation-solute interaction

FCC FeMnCoNi+Al complex concentrated alloys

Pseudo-ternary FeMnCoNi+Al FCC complex concentrated alloys predicted by the edge dislocation model. Adapted from study

BCC screw dislocation-solute interaction

NbMoW BCC alloys

Ternary NbMoW BCC alloys predicted by the screw dislocation-solute interaction model.

BCC screw dislocation-solute Suzuki model

BCC TiNbZr alloy

Yield stress-tempreature relationship of BCC TiNbZr alloy predicted by the Suzuki model.

Options for high-throughput calculations and materials selection

See this example Jupyter Notebook for high-throughput calculations.

You can use pySSpredict together with TC-python for both mechanical properties and phase stability predictions. See this Jupyter Notebook that runs on the cluster.

Install

  • Clone the project.
  • In the project directory:
    pip install .

Future: Model Implementations

  • Ductility model for BCC materials.

Owner

  • Login: Dongsheng-Wen
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Wen
    given-names: Dongsheng
    orcid: https://orcid.org/0000-0002-0728-0231
  - family-names: Titus
    given-names: Michael
    orcid: https://orcid.org/0000-0002-3423-4505
title: "pySSpredict: A python-based tool for predicting solid-solution strengthening"
version: 1.1.0

GitHub Events

Total
  • Release event: 1
  • Watch event: 4
  • Issue comment event: 1
  • Push event: 1
  • Pull request event: 2
  • Create event: 1
Last Year
  • Release event: 1
  • Watch event: 4
  • Issue comment event: 1
  • Push event: 1
  • Pull request event: 2
  • Create event: 1