AIMNet2

Predict quantum-informed molecular properties from 3D structures.

Input

Molecular structure

Upload one or more 3D molecular structures with explicit coordinates.

25 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job” — or start from an example.

Water single point

What is AIMNet2?

AIMNet2 is a neural network potential for quantum-informed molecular property prediction. Given a coordinate-bearing molecular structure, AIMNet2 predicts energies, forces, atomic charges, stress tensors, and Hessians from its trained model families.

AIMNet2 is designed for isolated molecules and molecular clusters rather than protein-scale biomolecular simulation. It is useful when you need rapid energy, force, charge, or second-derivative estimates for supported organic and elemental-organic chemistry.

How to use AIMNet2 online

ProteinIQ runs AIMNet2 on cloud GPU infrastructure with no Python setup. Upload one or more 3D structure files, choose the model family, and submit the job.

Input

Input	Details
`Molecular structure`	One to 20 XYZ, extended XYZ, PDB, CIF, MCIF, or mmCIF files with atomic coordinates.

SMILES strings are not accepted because AIMNet2 operates on explicit 3D coordinates. Generate a conformer first, then upload the coordinate-bearing structure.

Settings

Setting	Description
`Model`	Selects the AIMNet2 model alias. `aimnet2` is the general wB97M-D3 model. `aimnet2-2025` is the recommended B97-3c model. `aimnet2-nse` is intended for radicals and open-shell systems. `aimnet2-pd` supports palladium chemistry. `aimnet2-rxn` is a reaction model for neutral H/C/N/O systems.
`Charge`	Net molecular charge passed to AIMNet2. The reaction model requires charge 0.
`Multiplicity`	Spin multiplicity. Use `aimnet2-nse` for radicals or open-shell systems.
`Calculate forces`	Returns per-atom forces in eV/A. Enabled by default.
`Calculate stress tensor`	Requests a stress tensor for periodic inputs. Non-periodic molecules cannot return stress.
`Calculate Hessian`	Requests the 3N x 3N Hessian in eV/A^2. Hessians are expensive, non-periodic, and incompatible with DSF, Ewald, or PME Coulomb settings.
`Optimize geometry`	Runs an ASE FIRE geometry optimization with AIMNet2 forces before final property reporting.
`Optimization steps`	Maximum FIRE optimization steps.
`Optimization fmax`	Force convergence threshold in eV/A.
`Ensemble uncertainty`	Runs all four AIMNet2 ensemble members for the selected model family and reports energy mean, variance, and standard deviation.
`Long-range Coulomb`	Optional AIMNet2 Coulomb method: default, simple, DSF, Ewald, or PME. DSF uses cutoff 15 A and alpha 0.2 by default. Ewald and PME use accuracy 1e-6 by default.
`D3 cutoff`	Dispersion cutoff in Angstrom. The AIMNet2 default is 15 A with smoothing fraction 0.2.

Outputs

The Results table reports one row per submitted structure:

Column	Description
`Input`	Input file or structure name
`Model`	AIMNet2 model alias
`Atoms`	Atom count
`Charge`	Net molecular charge used for calculation
`Multiplicity`	Spin multiplicity used for calculation
`Energy (eV)`	Total predicted molecular energy
`Energy (kcal/mol)`	Energy converted from eV
`Max force (eV/A)`	Largest force-vector norm when forces are requested
`Charge sum (e)`	Sum of AIMNet2 atomic charges
`Dipole moment (eA)`	Norm of the AIMNet2 dipole vector
`Ensemble members`	Number of model members used when ensemble uncertainty is enabled
`Ensemble mean energy (eV)`	Mean energy across ensemble members
`Ensemble energy SD (eV)`	Standard deviation of ensemble-member energies
`Optimization converged`	Whether geometry optimization reached the requested force threshold
`Optimization steps`	Number of optimization steps completed
`Stress available`	Whether stress output was returned
`Hessian available`	Whether Hessian output was returned

Downloadable files include:

File	Description
`aimnet2_summary.json`	Settings, scalar properties, and file manifest
`aimnet2_atomic_charges.csv`	Atom index, element, coordinates, and AIMNet2 charge
`aimnet2_forces.csv`	Atom index, element, coordinates, and force components when forces are requested
`aimnet2_stress.csv`	3 x 3 stress tensor when stress is requested and supported
`aimnet2_hessian.csv`	Hessian matrix when requested and supported
`aimnet2_optimized.*`	Final optimized coordinates when geometry optimization is enabled
`aimnet2_optimization.log`	ASE FIRE optimization log when geometry optimization is enabled
`aimnet2_ensemble_uncertainty.csv`	Per-member ensemble energies when ensemble uncertainty is enabled

Batch jobs prefix downloadable filenames with each input name so files remain unique.

Model coverage

The standard AIMNet2 models support H, B, C, N, O, F, Si, P, S, Cl, As, Se, Br, and I. The Pd model supports H, B, C, N, O, F, Si, P, S, Cl, Se, Br, Pd, and I. The reaction model supports H, C, N, and O only and requires a neutral net charge.

Use aimnet2-nse for open-shell systems. Use aimnet2-pd for supported palladium chemistry. Use aimnet2-rxn only for neutral H/C/N/O reaction systems.

Limitations

AIMNet2 is trained for isolated molecules and molecular clusters, not bulk materials, extended surfaces, or metallic systems.
Alkali and alkaline earth metals are not supported. Transition metals are not supported except Pd in aimnet2-pd.
aimnet2-rxn only supports neutral H/C/N/O systems.
Standard models do not include implicit solvation. The Pd model includes the training solvation assumptions from its model family.
Multi-reference chemistry and systems far outside the training chemistry should be interpreted cautiously.

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What is AIMNet2?

How to use AIMNet2 online

ProteinIQ runs AIMNet2 on cloud GPU infrastructure with no Python setup. Upload one or more 3D structure files, choose the model family, and submit the job.