Obtain AceFF NNP models#

You will learn: how to download an AceFF neural-network checkpoint manually, for offline use or version pinning.

Prerequisites:

  • ACEMD installed with NNP support.

AceFF is the family of neural-network potentials developed by Acellera. The current release is AceFF 2.0, distributed under the Apache 2.0 license — no HuggingFace account, no access request, no token needed.

Note

For most users, the easiest path is to let ACEMD download and cache the model automatically — just set nnp.name: AceFF-2.0 in your input.yaml and skip this page. See Run an NNP/MM simulation or Run a pure NNP simulation.

This page covers the manual flow for offline machines, version pinning, or sharing a checkpoint across a cluster.

Download#

Pull the checkpoint directly:

curl -L -o aceff_v2.0.ckpt https://huggingface.co/Acellera/AceFF-2.0/resolve/main/aceff_v2.0.ckpt

Reference the resulting file from your input.yaml with nnp.name: TorchMD-Net and an explicit nnp.file:

input.yaml#
nnp:
  file: aceff_v2.0.ckpt
  name: TorchMD-Net
  type: torch

What AceFF 2.0 supports#

  • Elements: H, B, C, N, O, F, Si, P, S, Cl, Br, I.

  • Total charge: -2, -1, 0, +1, +2.

  • Recommended timestep: 2 fs (with hydrogen-mass repartitioning).

  • Architecture: TensorNet v2 inside the TorchMD-Net runtime.

Gotchas#

Warning

Small molecules only. AceFF 2.0 is trained on a curated PubChem dataset of small molecules. Proteins, water, and other biomolecular polymers are not in the training set — applying AceFF 2.0 to those species (in either pure NNP or as the NNP region of an NNP/MM run) will give wrong forces. For NNP/MM, restrict nnp.sel to a single small-molecule ligand or cofactor.

Warning

Total charges outside the -2…+2 range are not supported. Charged species at the edge of this range can still extrapolate poorly — if you see crashes, drop the timestep to 1 fs or fall back to an NNP/MM run with a smaller NNP region.

See also#