Python interface to set up coarse-grained simulations with state-of-the-art neural network potentials.
To install nnpcg we recommend creating two different conda environments as follows:
git clone https://github.com/tieleman-lab/nnpcg
cd nnpcg
conda env create -f nnpcg_train_run.yml
conda env create -f nnpcg_analysis.yml
conda activate nnpcg_dev
git clone https://github.com/torchmd/torchmd-net.git
cd torchmd-net
pip install -e .Then you can install nnpcg via pip in both environments:
cd ..
pip install -e .
conda activate nnpcg_ana
pip install -e .| NMRLipids Score | NMRLpids Benchmark | Trajectory |
|---|---|---|
| 0.83 | link | link |
| 0.76 | link | link |
| 0.73 | link | link |
| 0.69 | link | link |
| 0.61 | link | link |
If you find a bug in the source code, you can help us by submitting an issue to our GitHub repo. Even better, you can submit a Pull Request with a fix.
We really appreciate your feedback!
Source code included in this project is available under the MIT License.
Copyright (c) 2023, Daniel P. Ramirez
Project based on the Computational Molecular Science Python Cookiecutter version 1.1.