v2.0.0

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