Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes
This is the code that was used for the analysis conducted in our paper:
Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes.
The repository contains code to generate smoothed or standard radial distribution functions.
It also contains my implementation of the Variational Mixture of Gaussians, outlined by C. Bishop in C10, Pattern Recognition and Machine Learning.
descriptor_generator.py takes as input a series of csv files. These files should be formatted such
that the second, third and fourth columns correspond to the x, y and z coordinates of a
particular particle and the fifth column corresponds to the label of that particle.
An example of how to generate descriptors from csv files in this format:
python descriptor_generator.py --directory='1.0-80/' --dataset=1080 --num_files=20 --smoothed=True
An example of how to run the Variational Mixture of Gaussians code on prepared data:
python inference.py
This code was implemented using Python 3.7.6 and the following packages:
- numpy (1.18.1)
- pandas (1.0.1)
- scikit-learn (0.22.1)
- scipy (1.4.1)
If you use this code for your research, please cite or acknowledge the author (Penelope Jones, pj321@cam.ac.uk). Please feel free to contact me if you have any questions about this work.