molpot is designed for efficient building, training and deployment of molecular simulation potentials, leveraging a modular design to enable flexible customization and seamless integration into different workflows. It incorporates cutting-edge techniques, such as torch.compile for optimized deep learning execution and high-performance C++ operators for computational efficiency. For more information, please refer to the documentation.
- Hybrid and Modular Neural Network and Classical Potential;
- High-Performance C++ Operators;
- Built-in torch-based MD simulation;
- Versatile Data Pipeline;
- User-Friendly App Wrapping
To install molpot, you can use pip:
pip install molpotor clone the repository and install it manually:
git clone http:
cd molpot
pip install .or install from docker
docker pull molpot/molpot:latestor apptainer by using definition file
apptainer build molpot.sif molpot.defInstead of giving getting started guide here, we prefer to give a tutorial about how to learn this package, since molpot is a general and extensible package. Once you have a requirements, find a similar example or tutorial, read the code and check the docstring, and you will get a good understanding. Here is a table of content of the doc:
- tutorial: A how-to notebook driven by tasks
- examples: A collection of examples
- getting started: Introduce the basic concepts
- API: A complete API reference
- contributing: how to customize your own module
TO BE ANNOUNCED
TO BE ANNOUNCED