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.
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 | 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.
| 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. |
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.
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.
aimnet2-pd.aimnet2-rxn only supports neutral H/C/N/O systems.
Compute 200+ RDKit molecular descriptors, drug-likeness rule violations, and structural fingerprints for QSAR, virtual screening, and ML workflows
Analyze noncovalent interactions in protein-ligand complex structures with PLIP, including hydrogen bonds, hydrophobic contacts, pi interactions, salt bridges, water bridges, halogen bonds, and metal complexes.
PoseBusters validates generated or docked molecular poses with chemically and structurally grounded quality checks for molecular geometry, intermolecular interactions, and optional reference-pose agreement.
ProLIF calculates protein-ligand interaction fingerprints from 3D structures, returning residue-level interaction tables, interaction metadata, and native fingerprint files.
Predict metal and water binding sites in protein structures using 3D convolutional neural networks (AllMetal3D + Water3D).
Predict protein hydration sites from a structure using a diffusion model with ESM features and a confidence-filtering head.
Predict protein aggregation nucleation propensity from amino acid sequences using the Lehner Lab CANYA neural network.
Faithful static-mode Aggrescan3D tool for per-residue aggregation propensity analysis from a single protein structure.
Score protein mutations with evolutionary profiles from homologous sequences and inverse folding. EvoIF returns a dimensionless log-odds score for each submitted single or multi-site mutation.
Predict pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure. PROPKA calculates environment-driven pKa shifts for standard ionizable residues, terminal groups, and supported ligand atom types.
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.
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 | 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.
| 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. |
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.
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.
aimnet2-pd.aimnet2-rxn only supports neutral H/C/N/O systems.Compute 200+ RDKit molecular descriptors, drug-likeness rule violations, and structural fingerprints for QSAR, virtual screening, and ML workflows
Analyze noncovalent interactions in protein-ligand complex structures with PLIP, including hydrogen bonds, hydrophobic contacts, pi interactions, salt bridges, water bridges, halogen bonds, and metal complexes.
PoseBusters validates generated or docked molecular poses with chemically and structurally grounded quality checks for molecular geometry, intermolecular interactions, and optional reference-pose agreement.
ProLIF calculates protein-ligand interaction fingerprints from 3D structures, returning residue-level interaction tables, interaction metadata, and native fingerprint files.
Predict metal and water binding sites in protein structures using 3D convolutional neural networks (AllMetal3D + Water3D).
Predict protein hydration sites from a structure using a diffusion model with ESM features and a confidence-filtering head.
Predict protein aggregation nucleation propensity from amino acid sequences using the Lehner Lab CANYA neural network.
Faithful static-mode Aggrescan3D tool for per-residue aggregation propensity analysis from a single protein structure.
Score protein mutations with evolutionary profiles from homologous sequences and inverse folding. EvoIF returns a dimensionless log-odds score for each submitted single or multi-site mutation.
Predict pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure. PROPKA calculates environment-driven pKa shifts for standard ionizable residues, terminal groups, and supported ligand atom types.