AtomVoxelizer 0.2.1

Periodic atom-centered voxel grids for atomistic structures.

AtomVoxelizer

AtomVoxelizer builds periodic atom-centered voxel grids for atomistic structures. The core VoxelGrid class stores a 3D NumPy grid over a periodic cell and provides helpers for adding, setting, scaling, sampling, and plotting spherical regions.

Installation

Install the released package from PyPI:

pip install AtomVoxelizer

Install from the GitLab repository for development:

git clone https://gitlab.com/tgmaxson/atomvoxelizer.git
cd atomvoxelizer
pip install -e ".[dev,examples]"

Install optional acceleration backends directly if you need them:

pip install numba
pip install taichi
# Choose the CuPy package matching your CUDA runtime, for example:
pip install cupy-cuda12x
pip install ".[analysis]"

VoxelGrid is always the NumPy backend. Optional acceleration backends are explicit: VoxelGridNumba, VoxelGridTaichi, and VoxelGridCuPy. VoxelGridAnalysis uses scikit-image for connected-volume and marching-cubes analysis when the analysis extra is installed. The examples extra installs ASE for CIF loading and Wulff construction examples.

Basic Usage

import numpy as np

from atomvoxelizer import VoxelGrid

cell = np.eye(3) * 10.0
grid = VoxelGrid(cell=cell, resolution=0.25)

grid.add_sphere(center=np.array([5.0, 5.0, 5.0]), radius=1.0, value=1.0)
grid.set_sphere(center=np.array([2.0, 2.0, 2.0]), radius=0.5, value=-1.0)
grid.clamp_grid(min_val=-1.0, max_val=1.0)

Sphere operations accept two masks. mask="constant" writes the supplied value or factor across the sphere. mask="distance" writes the real-space distance from the sphere center at each voxel. Combining a distance mask with min_spheres gives a nearest-atom distance field:

from atomvoxelizer import VoxelGridAnalysis

grid.grid.fill(np.inf)
grid.min_spheres(atom_positions, cutoff_radii, mask="distance")

analysis = VoxelGridAnalysis(grid)
vertices, faces = analysis.mesh_at_value(2.0, periodic=True)
surface_area = analysis.mesh_surface_area(vertices, faces)

Periodic scalar meshes are clipped at the primary cell boundary so triangles that cross a periodic boundary are cut at the cell edge.

Zeolite Example

The zeolite example and CIF files live in examples/zeolite/.

pip install -e ".[examples]"
python examples/zeolite/zeolite_voxel.py BEA

The script reads a framework CIF, builds voxel grids at several resolutions, plots middle XZ slices, benchmarks supercell scaling, and opens a 3D scatter plot.

The analysis example estimates pore volume and internal surface area:

pip install -e ".[examples,analysis]"
python examples/zeolite/zeolite_analysis.py BEA --resolution 0.25
python examples/zeolite/zeolite_analysis.py BEA --convergence 1.0 0.75 0.5 --plot bea_convergence.png

Wulff Distance-Surface Example

The Wulff example builds a nanoparticle, voxelizes the nearest-atom distance field, and exports a marching-cubes mesh at a requested distance:

pip install -e ".[examples,analysis]"
python examples/wulff/distance_surface.py --symbol Pt --size 147 --distance 2.0 --output pt_surface.npz
python examples/wulff/distance_surface.py --symbol Pt --size 147 --distance 2.0 --plot pt_surface.png
python examples/wulff/distance_surface.py --symbol Pt --size 147 --distance 2.0 --show

Tests and Benchmarks

Run the correctness tests with:

pytest

Run the backend benchmark with:

python benchmarks/benchmark_backends.py --backends numpy numba taichi cupy
python benchmarks/benchmark_backends.py --zeolite-scaling --framework BEA --resolution 0.5 --plot zeolite_scaling.png
python benchmarks/benchmark_backends.py --workload zeolite --backends taichi-gpu

Run the built-in structure benchmarks for a zeolite and a roughly 1000 atom Wulff construction with:

python benchmarks/benchmark_structures.py

Backends whose optional dependencies are not installed are reported as missing.

Documentation

Documentation is scaffolded with Sphinx for Read the Docs.

Build it locally with:

pip install -e ".[docs]"
sphinx-build -b html docs/source docs/build/html

Read the Docs can use .readthedocs.yaml directly.

Publishing

Build and check PyPI artifacts with:

pip install -e ".[publish]"
python -m build
twine check dist/*

Upload to TestPyPI first, then PyPI:

twine upload --repository testpypi dist/*
twine upload dist/*