This is the repository for my senior design project and thesis, titled "A Machine Leraning Approach to Study Li+ Dynamics in Solid Polymer Electrolytes." Project was advised by Dr. Dmitry Bedrov and Aditya Choudhary from the University of Utah's Materials Science and Engineering Department.
- data: data that was used for the machine learning task
- figures: any figures that were produced by Python code
- model_saves: selected saved models for use in different notebooks
- tables: summary of model scores on test dataset, used for tables in thesis
- thesis_figures: any figures used in the thesis (some are repeats from figures)
As the name implies, the notebook titled 1_exploratory_analysis.ipnyb goes through the exploratory analysis of the data before the supervised learning tasks are performed. The notebook covers the following:
- Correlation heatmaps
- Distribution plotting (violin plots, histograms, KDEs)
- K-sample Anderson-Darling tests
- A UMAP embedding of the featurized data
The notebook titled 2_full_analysis.ipynb includes the code and explanations for the supervised learning tasks:
- Use only structural features (coordination numbers) as features to predict Li+ displacement
- Use structural and dynamic features (changes in coordination number over a given timestep) to predict Li+ displacement
Each task has a regression and classification approach.
More of an introduction to feature importances than anything else, 3_feature_importances.ipynb has the code for the following:
- Impurity-based feature importance for tree-based models
- Permutation importance from scikit-learn
- SHAP feature importance
Explanations for this notebook are sparse, since feature importance really isn't useful for models with poor predictive ability.
4_volume_plots.ipynb is a short and sweet notebook adapted from a Plotly example. It was used to make some volume plots for one of the thesis figures.