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https://github.com/tummfm/relative-entropy
Deep Coarse-grained Potentials via Relative Entropy Minimization
https://github.com/tummfm/relative-entropy
Last synced: 11 days ago
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Deep Coarse-grained Potentials via Relative Entropy Minimization
- Host: GitHub
- URL: https://github.com/tummfm/relative-entropy
- Owner: tummfm
- License: apache-2.0
- Created: 2022-08-09T14:52:49.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-22T15:35:51.000Z (over 1 year ago)
- Last Synced: 2024-08-01T16:54:22.002Z (3 months ago)
- Language: Python
- Homepage:
- Size: 24.6 MB
- Stars: 10
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Relative Entropy Minimization
Implementation of the Relative Entropy Minimization and Force Matching methods
as employed in the paper
[Deep coarse-grained potentials via relative entropy minimization](https://aip.scitation.org/doi/10.1063/5.0124538).## Getting started
This repository provides code to train and simulate two systems, coarse-grained
water and alanine dipeptide, with force matching and relative
entropy minimization. The training examples can be found in
[CG_water_force_matching.py](examples/water/CG_water_force_matching.py)
and [CG_water_relative_entropy.py](examples/water/CG_water_relative_entropy.py)
for water and in [alanine_force_matching.py](examples/alanine_dipeptide/alanine_force_matching.py)
and [alanine_relative_entropy.py](examples/alanine_dipeptide/alanine_relative_entropy.py)
for alanine dipeptide. Training the model with force matching will take a few
hours and more than a day with relative entropy.MD simulation employing the trained DimeNet++ models can be found
in [CG_water_simulation.py](examples/water/CG_water_simulation.py)
and [alanine_simulation.py](examples/alanine_dipeptide/alanine_simulation.py)
respectively.## Data sets
The data sets for alanine dipeptide and water can be downloaded from Google
Drive via the following link:
[https://drive.google.com/drive/folders/1IBZbuSBIBhvFbVhuo9s-ENE2IyWG-YI_?usp=sharing](https://drive.google.com/drive/folders/1IBZbuSBIBhvFbVhuo9s-ENE2IyWG-YI_?usp=sharing)
Once downloaded, you can move the conf and force files into the dataset folder
of [water](examples/water/data/dataset) and
[alanine dipeptide](examples/alanine_dipeptide/data/dataset).## Installation
All dependencies can be installed locally with pip:
```
pip install -e .[all]
```However, this only installs a CPU version of Jax. If you want to enable GPU
support, please overwrite the jaxlib version:
```
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
```## Requirements
The repository uses with the following packages:
```
'jax>=0.4.3',
'jax-md>=0.2.5',
'optax>=0.0.9',
'dm-haiku>=0.0.9',
'sympy',
'cloudpickle',
'chex',
'jax-sgmc',
```
The code was run with Python 3.8. The packages used in the paper
are listed in [setup.py](setup.py).## Citation
Please cite our paper if you use this code in your own work:
```
@article{thaler_entropy_2022,
title = {Deep coarse-grained potentials via relative entropy minimization},
author = {Thaler, Stephan and Stupp, Maximilian and Zavadlav, Julija},
journal={The Journal of Chemical Physics},
volume = {157},
number = {24},
pages = {244103},
year = {2022},
doi = {10.1063/5.0124538}
}
```