Research Data Leeds Repository

Supporting data for "Using Neural Networks to Deduce Polymer Molecular Weight Distributions from Linear Rheology"

Citation

Elliott, Robert J. and Cutillo, Luisa and Chinmay, Das and Mattsson, Johan and Read, Daniel J. (2025) Supporting data for "Using Neural Networks to Deduce Polymer Molecular Weight Distributions from Linear Rheology". University of Leeds. [Dataset] https://doi.org/10.5518/1689¹

Dataset description

Supporting data sufficient to reproduce the results of the related publication. The goal of the work is to predict the molecular weight distribution from the linear melt rheology of polystyrene. This dataset inlcudes testing data, python code, neural network models, and data used to setup conda environment for smooth running of python code.

Divisions:	Faculty of Engineering and Physical Sciences > School of Mathematics2
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	19 Jun 2025 14:22
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/14283
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Elliott, Robert J. https://orcid.org/0009-0005-4718-20024 Dutch Polymer Institute https://ror.org/01m71fs94 ror University of Leeds https://ror.org/024mrxd33 ror Cutillo, Luisa https://orcid.org/0000-0002-2205-03385 University of Leeds https://ror.org/024mrxd33 ror Chinmay, Das https://orcid.org/0000-0002-1454-62106 University of Leeds https://ror.org/024mrxd33 ror Mattsson, Johan University of Leeds https://ror.org/024mrxd33 ror Read, Daniel J. University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: DPI Grant numbers: 861 Publication date: 2025 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: py19rje7 Last Modified: 02 Jul 2025 13:50

Files

Documentation

Data_Sources.txt [1kB] 8 Data_Sources.txt [1kB] 9 Data_Sources.txt [1kB] 10 Security: Anyone File name: Data_Sources.txt11 Content type: Documentation Description: Description of where the data came from for all PS samples Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 1kB

README_Elliott-etal_2025.txt [4kB] 12 README_Elliott-etal_2025.txt [4kB] 13 README_Elliott-etal_2025.txt [4kB] 14 Security: Anyone File name: README_Elliott-etal_2025.txt15 Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 4kB

Data

GPC_Data.zip [23kB] 16 GPC_Data.zip [23kB] 17 GPC_Data.zip [23kB] 18 Security: Anyone File name: GPC_Data.zip19 Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 23kB

NN_Models.zip [294MB] 20 NN_Models.zip [294MB] 21 NN_Models.zip [294MB] 22 Security: Anyone File name: NN_Models.zip23 Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 294MB

Rheo_Data.zip [18kB] 24 Rheo_Data.zip [18kB] 25 Rheo_Data.zip [18kB] 26 Security: Anyone File name: Rheo_Data.zip27 Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 18kB

conda_env_PS.yml [326B] 28 conda_env_PS.yml [326B] 29 conda_env_PS.yml [326B] 30 Security: Anyone File name: conda_env_PS.yml31 Content type: Data Description: .yml file for quick creation of conda environment that will install all dependencies to allow the python file to run. License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 326B

Program

PS_MWD_Prediction_UPDATED.py [11kB] 32 PS_MWD_Prediction_UPDATED.py [11kB] 33 PS_MWD_Prediction_UPDATED.py [11kB] 34 Security: Anyone File name: PS_MWD_Prediction_UPDATED.py35 Content type: Program Description: python file to predict the MWD from rheology for any of the PS samples included Mime-Type: text/x-python License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 11kB Note: File updated 02/07/2025

• 1. https://doi.org/10.5518/1689
• 2. https://archive.researchdata.leeds.ac.uk/view/divisions/SM/
• 3. https://archive.researchdata.leeds.ac.uk/id/eprint/1428
• 4. https://orcid.org/0009-0005-4718-2002
• 5. https://orcid.org/0000-0002-2205-0338
• 6. https://orcid.org/0000-0002-1454-6210
• 7. mailto:py19rje
• 8. https://archive.researchdata.leeds.ac.uk/1428/6/Data_Sources.txt
• 9. https://archive.researchdata.leeds.ac.uk/1428/6/Data_Sources.txt
• 10. https://archive.researchdata.leeds.ac.uk/1428/6/Data_Sources.txt
• 11. https://archive.researchdata.leeds.ac.uk/1428/6/Data_Sources.txt
• 12. https://archive.researchdata.leeds.ac.uk/1428/7/README_Elliott-etal_2025.txt
• 13. https://archive.researchdata.leeds.ac.uk/1428/7/README_Elliott-etal_2025.txt
• 14. https://archive.researchdata.leeds.ac.uk/1428/7/README_Elliott-etal_2025.txt
• 15. https://archive.researchdata.leeds.ac.uk/1428/7/README_Elliott-etal_2025.txt
• 16. https://archive.researchdata.leeds.ac.uk/1428/1/GPC_Data.zip
• 17. https://archive.researchdata.leeds.ac.uk/1428/1/GPC_Data.zip
• 18. https://archive.researchdata.leeds.ac.uk/1428/1/GPC_Data.zip
• 19. https://archive.researchdata.leeds.ac.uk/1428/1/GPC_Data.zip
• 20. https://archive.researchdata.leeds.ac.uk/1428/2/NN_Models.zip
• 21. https://archive.researchdata.leeds.ac.uk/1428/2/NN_Models.zip
• 22. https://archive.researchdata.leeds.ac.uk/1428/2/NN_Models.zip
• 23. https://archive.researchdata.leeds.ac.uk/1428/2/NN_Models.zip
• 24. https://archive.researchdata.leeds.ac.uk/1428/3/Rheo_Data.zip
• 25. https://archive.researchdata.leeds.ac.uk/1428/3/Rheo_Data.zip
• 26. https://archive.researchdata.leeds.ac.uk/1428/3/Rheo_Data.zip
• 27. https://archive.researchdata.leeds.ac.uk/1428/3/Rheo_Data.zip
• 28. https://archive.researchdata.leeds.ac.uk/1428/4/conda_env_PS.yml
• 29. https://archive.researchdata.leeds.ac.uk/1428/4/conda_env_PS.yml
• 30. https://archive.researchdata.leeds.ac.uk/1428/4/conda_env_PS.yml
• 31. https://archive.researchdata.leeds.ac.uk/1428/4/conda_env_PS.yml
• 32. https://archive.researchdata.leeds.ac.uk/1428/8/PS_MWD_Prediction_UPDATED.py
• 33. https://archive.researchdata.leeds.ac.uk/1428/8/PS_MWD_Prediction_UPDATED.py
• 34. https://archive.researchdata.leeds.ac.uk/1428/8/PS_MWD_Prediction_UPDATED.py
• 35. https://archive.researchdata.leeds.ac.uk/1428/8/PS_MWD_Prediction_UPDATED.py