Migration Guide (v0.3.0)

Starting with version 0.3.0, PyTorch is now an optional dependency. The previous PyTorch-based functionality has been moved to a separate nvalchemiops.torch namespace. This guide provides a mapping of old import paths to new ones.

Import Path Changes

Old Import Path	New Import Path
from nvalchemiops.interactions.dispersion import dftd3	from nvalchemiops.torch.interactions.dispersion import dftd3
from nvalchemiops.interactions.dispersion import D3Parameters	from nvalchemiops.torch.interactions.dispersion import D3Parameters
from nvalchemiops.neighbors import neighbor_list	from nvalchemiops.torch.neighbors import neighbor_list
from nvalchemiops.neighbors import estimate_max_neighbors	from nvalchemiops.torch.neighbors.neighbor_utils import estimate_max_neighbors
from nvalchemiops.neighborlist import neighbor_list	from nvalchemiops.torch.neighbors import neighbor_list
from nvalchemiops.neighborlist import cell_list	from nvalchemiops.torch.neighbors import cell_list

Backwards Compatibility

The old import paths will continue to work but will emit DeprecationWarning messages. They will be removed in a future release.

Naive PBC Metadata Changes

Advanced callers that precompute periodic metadata for naive neighbor-list methods should update cached arguments as follows:

Old Cached Inputs	New Cached Inputs
shift_range_per_dimension, shift_offset, total_shifts	shift_range_per_dimension, num_shifts_per_system, max_shifts_per_system

The public Torch and JAX APIs now decode periodic shifts on-the-fly inside the neighbor kernels. Materialized shift buffers and shift_offset / total_shifts are no longer part of the public naive-PBC workflow.

Warp Kernels

If you need direct access to the underlying Warp kernels (without PyTorch), use the non-torch namespaces:

• nvalchemiops.neighbors - Warp neighbor list kernels

• nvalchemiops.interactions.dispersion - Warp dispersion kernels

• nvalchemiops.interactions.electrostatics - Warp electrostatics kernels

• nvalchemiops.math - Warp math and spline kernels

These modules comprise both targeted kernels as well as end-to-end launchers where possible, which run the full workflow based on warp.arrays.