Title: Ionic Conductivity Prediction of Solid Polymer Electrolytes Using Transformer Representations

An Honours Project 1 (FYP) submitted to the School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University in partial fulfillment of the requirements for the degree of Bachelor of Science in Chemistry and Biological Chemistry.

$ pip install -r requirements.txt

Python version: 3.12.2
OS Platform: Windows
OS Version: Windows 10

All package requirements can be found in requirements.txt.

Use run.sh to execute the files after installing required packages. You can pass the function name of each step of the pipeline consecutively as arguments when calling main.py. By default it currently runs all steps from data_cleaning to cv.

Make use of src/config.py to change run settings.

1. Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery

@article{doi:10.1021/acscentsci.2c01123,
author = {Bradford, Gabriel and Lopez, Jeffrey and Ruza, Jurgis and Stolberg, Michael A. and Osterude, Richard and Johnson, Jeremiah A. and Gomez-Bombarelli, Rafael and Shao-Horn, Yang},
title = {Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery},
journal = {ACS Central Science},
volume = {9},
number = {2},
pages = {206-216},
year = {2023},
doi = {10.1021/acscentsci.2c01123},

URL = { 
    
        https://doi.org/10.1021/acscentsci.2c01123
    
    

},
eprint = { 
    
        https://doi.org/10.1021/acscentsci.2c01123
    
    

}

}