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difftre

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Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

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# Differentiable Trajectory Reweighting

Reference implementation of the Differentiable Trajectory Reweighting (DiffTRe) 
method as implemented in the paper [Learning neural network potentials from 
experimental data via Differentiable Trajectory Reweighting](https://www.nature.com/articles/s41467-021-27241-4).

If you're interested in a more up-to-date version of DiffTRe, feel free to check out [chemtrain](https://github.com/tummfm/chemtrain)!

## Getting started
The Jupyter notebooks provide a guided tour through the DiffTRe method 
([double_well.ipynb](double_well.ipynb)) as well as its application to 
realistic systems in [diamond.ipynb](diamond.ipynb) and [CG_Water.ipynb](CG_Water.ipynb).

## Installation
All dependencies can be installed locally with pip:
```
pip install .
```

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[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
```

## Requirements
The repository uses the following packages:
```
    MDAnalysis
    jax>=0.2.12
    jaxlib>=0.1.65
    jax-md==0.1.13
    optax
    dm-haiku>=0.0.4
    sympy>=1.5
```
The code was run with Python 3.6. The exact versions used in the paper 
are listed in [requirements.txt](requirements.txt). MDAnalysis is only used for loading of 
initial MD states and can be omitted in case of installation issues.

## Contact

For questions, please contact stephan.thaler@tum.de.

## Citation
Please cite our paper if you use DiffTRe, the trained models or this code in 
your own work:
```
@article{thaler_difftre_2021,
  title = {Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting},
  author = {Thaler, Stephan and Zavadlav, Julija},
  journal={Nature Communications},
  volume={12},
  number={1},
  pages={6884--6884},
  doi={10.1038/s41467-021-27241-4}
  year = {2021}
}
```

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