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https://github.com/MMunibas/Asparagus
Program Package for Sampling, Training and Applying ML-based Potential models
https://github.com/MMunibas/Asparagus
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Program Package for Sampling, Training and Applying ML-based Potential models
- Host: GitHub
- URL: https://github.com/MMunibas/Asparagus
- Owner: MMunibas
- License: other
- Created: 2024-07-08T13:44:56.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-13T11:35:52.000Z (about 1 month ago)
- Last Synced: 2024-12-13T12:32:08.266Z (about 1 month ago)
- Language: Python
- Size: 11 MB
- Stars: 8
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- best-of-atomistic-machine-learning - GitHub
README
# Project Asparagus
**Authors**: K. Toepfer, L.I. Vazquez-Salazar
## What is this?
- A refined implementation of PhysNet and PainNN (and more atomistic NN to come) in PyTorch.
- A Suit for the automatic construction of Potential Energy Surface (PES) from sampling to production.
## How to use?
- Clone the repository
- Requirements:
- Python 
3.8
- PyTorch 1.10
- Atomic Simulation Environment (ASE) 3.21
- Torch-ema 0.3
- TensorBoardX 2.4
- numpy, scipy, pandas, ...
### Setting up the environment
We recommend to use [Mamba](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html) for the creation of a virtual environment.
Once in mamba, you can create a virtual enviroment called *asparagus*
```
mamba create --name asparagus python=3.8
```
To activate the virtual environment use the command:
```
mamba activate asparagus
```
### Installation
Installation must be done in the virtual environment through pip. It is important to mention that the path where you are
working will be added to the *PYTHONPATH*, so you can import the modules from anywhere.
Install via pip:
```
python -m pip install .
```
Alternatively, but deprecated, install via setup.py:
```
python setup.py install
```
**BEWARE**: With this command any modification that is done to the code in the folder *asparagus* will be automatically reflected
in the modules that you import.
**NOTE**: Everytime you want to import the module, you must use the following command:
```
from asparagus import Asparagus
```
Then Asparagus is a function that takes some arguments.
## Documentation
Please check our documentation [here](http://asparagus-bundle.readthedocs.io/en/latest/)
## What needs to be added?
- [ ] Add more NN architectures (Low priority)
- [x] Read parameters from older PhysNet Versions (i.e. TF1 and TF2) (Luis)
- [ ] Add sampling methods:
- [x] MD with XTB
- [x] MC with XTB
- [x] Normal Model Sampling (Vanilla with random generation)
- [x] Normal Model Scanning
- [ ] Umbrella Sampling (Low priority)
- [x] Metadynamics Sampling
- [ ] Electronic structure calculations:
- [x] ASE calculator (As good as it can be)
- [x] Automatic generation of input files for commonly used codes (e.g. Gaussian, Orca, MOLPRO, etc.)
- [x] Automatic extraction of information from output files
- Trainer class:
- [ ] Training of model ensemble
- Tester class:
- [x] Finish automatic evaluation
- Active learning
- [ ] Adaptive Sampling
- [ ] Uncertainty calculations
- [x] Model ensemble via ASE calculator
- [ ] Deep Evidential Regression (Low priority)
- Tools class:
- [x] Normal mode calculation (Luis)
- [x] Minimum energy path and Minimum dynamic path
- [x] Diffusion MonteCarlo
- [ ] Others(?)
- Production:
- [x] PyCharmm
- [x] ASE calculator for dynamics
- Documentation:
- [X] Improve documentation
- [x] Add examples
- [x] Add tutorials
- Others
- [ ] Create a conda package
- [ ] Create a pip package
## Contact
For any questions, please open an issue in the repository.
## How to cite
[](https://arxiv.org/abs/2407.15175)
[](https://doi.org/10.1016/j.cpc.2024.109446)
If you find this work useful in your research, please cite it as:
```latex
@article{asparagus_cpc,
title = {Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces},
author = {Kai Töpfer and Luis Itza Vazquez-Salazar and Markus Meuwly},
journal = {Computer Physics Communications},
volume = {308},
pages = {109446},
year = {2025},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2024.109446},
url = {https://www.sciencedirect.com/science/article/pii/S0010465524003692},
keywords = {Machine learning, Neural networks, Potential energy surfaces},
}
```