https://github.com/tummfm/relative-entropy

Deep Coarse-grained Potentials via Relative Entropy Minimization
https://github.com/tummfm/relative-entropy

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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}

}

```