SevenNet is a Python library implementing Scalable EquiVariance Enabled Neural Networks, primarily for atomistic simulations and materials science. It integrates with the Atomic Simulation Environment (ASE) for molecular dynamics, energy, and force calculations. The library is actively developed, with frequent minor releases and specific checkpoint releases for new pre-trained models. The current stable version is 0.12.1.
pip install sevennVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to use `SevenNetCalculator` to compute energy, forces, and stress for an ASE `Atoms` object using a pre-trained SevenNet model. Ensure you have a model checkpoint available locally.
Update your CLI commands to use the `sevenn <subcommand>` pattern. Refer to the official documentation for updated subcommand syntax.
Always install `sevenn` using its `pip install sevenn` command, which will handle `e3nn` dependencies. If manually managing `e3nn`, check `sevenn`'s `pyproject.toml` or `setup.py` for the exact required `e3nn` version range for your `sevenn` version.
Install `ninja` manually (`pip install ninja`) or install `sevenn` with the `[flashtp]` extra (e.g., `pip install sevenn[flashtp]`) if available, or manually install dependencies for the FlashTP feature.
Upgrade to `v0.12.1` or later and review your configuration files and CLI calls, especially those related to FlashTP, to ensure they use the standardized argument names.
Use the new subcommand-based CLI interface: `from sevenn.cli import run` and call `run(['train', ...])`, or execute `sevenn train ...` from the terminal.
Reinstall `sevenn` to ensure `e3nn` is installed correctly via its dependencies, or manually ensure your `e3nn` version matches the requirements for your `sevenn` version (e.g., `pip install 'e3nn>=0.5.0,<0.6.0'` if specified).
Install the `ninja` package: `pip install ninja`. This dependency is required for FlashTP's optimized kernel compilation.