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AIMNet2-rxn

A neural network interatomic potential specialized for closed-shell organic reactions (H, C, N, O), trained on ~4.7M reaction-relevant geometries at wB97M-D3/def2-TZVPP. Use for transition-state searches, NEB / batched-NEB, IRC profiles, and reaction-coordinate energy work.

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from aimnet.calculators import AIMNet2Calculator

# From the GCS-backed registry (alias):
calc = AIMNet2Calculator("aimnet2-rxn", ensemble_member=0)

# From Hugging Face Hub:
calc = AIMNet2Calculator("isayevlab/aimnet2-rxn", ensemble_member=0)

Legacy alias

The previously published short alias aimnet2rxn and member-level keys aimnet2_rxn_0 … aimnet2_rxn_3 still resolve and remain supported.

Both paths produce equivalent calculators. The HF path requires pip install "aimnet[hf]".

Calculator-enforced safeguards (this family)

The calculator applies the following checks automatically when validate_species=True (the default). Each can be bypassed with validate_species=False:

  • Element scope: input atomic numbers must be a subset of [1, 6, 7, 8]. Other elements raise ValueError with pointers to alternative families.
  • Net charge: only net-neutral systems (zwitterions OK). Non-zero charge raises ValueError pointing at aimnet2-wb97m-d3 for ions.
  • AFV row sanitization: at conversion time, atomic-feature-vector rows for elements outside [1, 6, 7, 8] are NaN-padded so validate_species=False produces NaN-propagation rather than plausible-looking nonsense.

Runtime corrections and safeguards fire regardless of validate_species:

  • Posthoc D3 dispersion: AIMNet2Calculator adds external DFT-D3(BJ) with the same wB97M-D3 parameters used by the default aimnet2 model (s6=1.0, s8=0.3908, a1=0.566, a2=3.128). This also applies to older rxn artifacts that do not carry needs_dispersion / d3_params metadata.
  • Hessian + torch.compile: setting both raises RuntimeError (Dynamo + double-backward through GELU is known to hang). Reconstruct with compile_model=False for TS / IRC / vibrational work.

A separate one-time UserWarning fires if the same Python process constructs calculators from two different AIMNet2 families (rxn vs. wb97m-d3 etc.), because their absolute energy scales are not comparable.

Canonical model card

Full content (energy convention, training data details, full limitations list, citation) lives at the Hugging Face model card:

https://huggingface.co/isayevlab/aimnet2-rxn

The HF README is the canonical source — this page summarizes only the integration mechanics.