This is a repository of SEGAL, a method to study Monte Carlo simulations in the semi-grand canonical ensemble with machine learning.

Updated work can be found in the revision submission folder.

Published article: https://www.nature.com/articles/s41524-022-00736-4

Examples were run in the environment defined by environment.yml file and conda:

conda env create -f environment.yml
conda activate segal