NVIDIA/physicsnemo v2.0.0

v2.0.0

Repository: NVIDIA/physicsnemo

Tag: v2.0.0

Published: 2026-03-10T23:47:04Z

Prerelease: no

Release notes: PhysicsNeMo General Release v2.0.0

📝 NVIDIA PhysicsNeMo v2.0 contains significant reorganization of all the features, with easier installation and integration to external packages. See the migration guide for more details!

Added

• Refactored diffusion preconditioners in

physicsnemo.diffusion.preconditioners relying on a new abstract base class BaseAffinePreconditioner for preconditioning schemes using affine transformations. Existing preconditioners (VPPrecond, VEPrecond, iDDPMPrecond, EDMPrecond) reimplemented based on this new interface.

• New physicsnemo.experimental.nn.symmetry module that implements building

blocks that preserve 2D and 3D rotational equivariance using a grid-based layout for efficient GPU parallelization, and an emphasis on compact einsum operations.

Changed

• PhysicsNemo v2.0 contains significant reorganization of tools. Please see

the v2.0-MIGRATION-GUIDE.md to understand what has changed and why.

• DiT (Diffusion Transformer) has been moved from physicsnemo.experimental.models.dit

to physicsnemo.models.dit.

Fixed

• Shape mistmatch bug in the Lennard Jones example

Dependencies

• CUDA backend is now selected via orthogonal cu12 / cu13 extras rather

than being hardcoded to CUDA 13. Feature extras (nn-extras, utils-extras, etc.) are now CUDA-agnostic and can be combined with either backend, e.g. pip install "nvidia-physicsnemo[cu13,nn-extras]". When neither cu12 nor cu13 is specified, PyTorch is installed from PyPI using its default build (currently CUDA 12.8 on Linux). For development with uv, use uv sync --extra cu13 (or --extra cu12) to select the backend.

Contributors

We’re grateful to everyone who contributed issues, feature ideas, fixes, and documentation updates — your input is what helps us continuously improve PhysicsNeMo for the whole community! A special shout-out to the authors of the pull requests listed above, in no particular order:

@jleinonen @dran-dev @aayushg55 @saikrishnanc-nv @jeis4wpi @albertocarpentieri @paveltomin @weilr @giprayogo @tonishi-nv @younes-abid @dakhare-creator @Alexey-Kamenev

Thank you :heart: — we truly appreciate your contributions and hope to see more from you in the future!