Machine Learning thermodynamic Perturbation Theory (MLPT) and Machine Learning Monte Carlo (MLMC)

This repository contains code that implement Machine Learning Perturbation Theory (MLPT) and Machine Learning Monte Carlo (MLMC) as used in references below.

Description

The mlpt.py script is designed to perform machine learning perturbation theory. The mlmc folder contains files necessary for performing machine learning Monte Carlo simulations. These tools have been developed and used by Dario Rocca, Tomáš Bučko, Mauricio Chagas da Silva, Basile Herzog.

Prerequisites

The scripts are written in Python 2 and Python 3. The following packages are required:

• ASE (Atomic Simulation Environment)
• NumPy
• SciPy
• Scikit-learn
• Joblib
• DScribe

Please ensure these packages are installed before running the scripts.

References

This code has been used in the following papers:

-Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: 
Density Functional Theory and Beyond: 
https://doi.org/10.1021/acs.jctc.1c01034

-Coupled cluster finite temperature simulations of periodic materials via machine learning:
10.26434/chemrxiv-2023-mvsxn

Please cite these papers if you use this code in your research.