vesin-torch 0.5.0
Computing neighbor lists for atomistic system, in TorchScript
Requires Python: >=3.10

# Vesin: fast neighbor lists for atomistic systems

[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](http://luthaf.fr/vesin/)
![Tests](https://img.shields.io/github/check-runs/Luthaf/vesin/main?logo=github&label=tests)

| English 🇺🇸⁠/⁠🇬🇧 | Occitan <img src="./docs/src/static/images/Occitan.png" width=18> | Arpitan <img src="./docs/src/static/images/Arpitan.png" width=18> | French 🇫🇷 | Gallo‑Italic <img src="./docs/src/static/images/Lombardy.png" width=18> | Catalan <img src="./docs/src/static/images/Catalan.png" width=18> | Spanish 🇪🇸 | Italian 🇮🇹 |
|------------------|----------|-----------|----------|--------------|---------|------------|------------|
| neighbo(u)r      | vesin    | vesin     | voisin   | visin        | veí     | vecino     | vicino     |


Vesin is a fast and easy to use library computing neighbor lists for atomistic
system. We provide an interface for the following programing languages:

- C (also compatible with C++). The project can be installed and used as a
  library with your own build system, or included as a single file and built
  directly by your own build system;
- Python;
- TorchScript, with both a C++ and Python interface;

### Installation

To use the code from Python, you can install it with `pip`:

```
pip install vesin
```

See the [documentation](https://luthaf.fr/vesin/latest/index.html#installation)
for more information on how to install the code to use it from C or C++.

### Usage instruction

You can either use the `NeighborList` calculator class:

```py
import numpy as np
from vesin import NeighborList

# positions can be anything compatible with numpy's ndarray
positions = [
    (0, 0, 0),
    (0, 1.3, 1.3),
]
box = 3.2 * np.eye(3)

calculator = NeighborList(cutoff=4.2, full_list=True)
i, j, S, d = calculator.compute(
    points=positions,
    box=box,
    periodic=True,
    quantities="ijSd"
)
```

We also provide a function with drop-in compatibility to ASE's neighbor list:

```py
import ase
from vesin import ase_neighbor_list

atoms = ase.Atoms(...)

i, j, S, d = ase_neighbor_list("ijSd", atoms, cutoff=4.2)
```

See the [documentation](https://luthaf.fr/vesin/latest/c-api.html) for more
information on how to use the code from C or C++.

### Benchmarks

You can find below benchmark result computing neighbor lists for increasingly
large diamond supercells, using an AMD 3955WX CPU and an NVIDIA 4070 Ti SUPER
GPU. You can run this benchmark on your system with the script at
`benchmarks/benchmark.py`. Missing points indicate that a specific code could
not run the calculation (for example, NNPOps requires the cell to be twice the
cutoff in size, and can't run with large cutoffs and small cells).

![Benchmarks](./docs/src/benchmark.png)

## License

Vesin is is distributed under the [3 clauses BSD license](LICENSE). By
contributing to this code, you agree to distribute your contributions under the
same license.

## Citation

If you found ``vesin`` useful, you can cite the pre-print where it was presented
(<https://doi.org/10.48550/arXiv.2508.15704>) as

```bibtex
@misc{metatensor-and-metatomic,
    title = {Metatensor and Metatomic: Foundational Libraries for Interoperable Atomistic
    Machine Learning},
    shorttitle = {Metatensor and Metatomic},
    author = {Bigi, Filippo and Abbott, Joseph W. and Loche, Philip and Mazitov, Arslan
    and Tisi, Davide and Langer, Marcel F. and Goscinski, Alexander and Pegolo, Paolo
    and Chong, Sanggyu and Goswami, Rohit and Chorna, Sofiia and Kellner, Matthias and
    Ceriotti, Michele and Fraux, Guillaume},
    year = {2025},
    month = aug,
    publisher = {arXiv},
    doi = {10.48550/arXiv.2508.15704},
}
```