MLPotential Keywords¶
Engine MLPotential¶
AIMNet2- Type:
Block
- Recurring:
False
- Description:
Options for the AIMNet2 machine learning potential backend.
CompileModel- Type:
Bool
- Default value:
No
- Description:
Compile the AIMNet2 model. This can improve performance for repeated calculations (e.g. during molecular dynamics), but increases startup cost.
EnsembleSize- Type:
Integer
- Default value:
4
- Value Range:
2 <= value <= 4
- Description:
Number of members in the ensemble. Uncertainty estimates improve with the number of members, but computational cost scales linearly.
LongRange- Type:
Block
- Recurring:
False
- Description:
Long-range electrostatics options.
DSF- Type:
Block
- Recurring:
False
- Description:
Damped shifted force Coulomb settings.
Alpha- Type:
Float
- Default value:
0.2
- Value Range:
value > 0
- Description:
Damping parameter for the DSF Coulomb method.
Cutoff- Type:
Float
- Default value:
15.0
- Unit:
Angstrom
- Value Range:
value > 0
- Description:
Cutoff distance in Angstrom for the DSF Coulomb neighbor list.
Ewald- Type:
Block
- Recurring:
False
- Description:
Accuracy settings for Ewald and PME Coulomb summation.
Accuracy- Type:
Float
- Default value:
1e-06
- Value Range:
value > 0
- Description:
Target accuracy for Ewald and PME summation. Smaller values are more accurate and more expensive.
Type- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, Simple, DSF, Ewald, PME]
- Description:
Long-range Coulomb method. Auto uses the AIMNet2 model default (typically simple for aperiodic systems, DSF for periodic systems). Simple uses all-pairs pairwise Coulomb, and is suitable for small aperiodic systems. DSF (damped shifted force) Coulomb with a finite cutoff is an efficient method for periodic and large aperiodic systems. Ewald and PME (Particle mesh Ewald) is for high accuracy periodic electrostatics.
UseEnsemble- Type:
Bool
- Default value:
No
- Description:
Whether to use an ensemble of independently trained model members for calculations. This gives uncertainty estimates for energies and forces, but at increased computational cost.
Backend- Type:
Multiple Choice
- Options:
[AIMNet2, FAIRChem, M3GNet, MACE, MatGL, NEP, TorchANI]
- Description:
The machine learning potential backend.
Device- Type:
Multiple Choice
- Default value:
- Options:
[, cpu, cuda:0, cuda:1]
- Description:
Device on which to run the calculation (e.g. cpu, cuda:0).
If empty, the device can be controlled using environment variables for TensorFlow or PyTorch.
FAIRChem- Type:
Block
- Recurring:
False
- Description:
Options for the FAIRChem machine learning potential backend.
ModelTask- Type:
String
- Default value:
- Description:
Model task to use if a custom UMA/eSEN model is supplied via a parameter file (e.g. ‘OC20’, ‘OC22’, ‘OC25’, ‘ODAC’, ‘OMat’, ‘OMC’, ‘OMol’). Ignored if a specific FAIRChem model is selected.
Fukui- Type:
Block
- Recurring:
False
- Description:
Expert settings for the charged states used to calculate Fukui functions and vertical ionization properties when AMS Properties%Other is requested.
Enabled- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, Yes, No]
- Description:
Controls calculation of the Fukui analysis when AMS Properties%Other is requested. Auto calculates it for supported aperiodic models, Yes explicitly requests it and reports a warning when it is unavailable, and No disables it.
FMinus- Type:
Block
- Recurring:
False
- Description:
Settings for the electron-removed state used to calculate Fukui f- and the vertical ionization energy.
ChargeDelta- Type:
Integer
- Default value:
1
- Value Range:
value > 0
- Description:
Change in total charge relative to the reference system. A value of 1 corresponds to removing one electron.
SpinState- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, Low, High, UnpairedElectrons]
- Description:
Spin state of the electron-removed system. Auto currently selects the lowest spin compatible with the reference spin and charge change. Low and High explicitly select the lowest or highest directly reachable spin, respectively. UnpairedElectrons uses the value of the UnpairedElectrons keyword.
UnpairedElectrons- Type:
Integer
- Default value:
0
- Value Range:
value >= 0
- Description:
Number of unpaired electrons in the electron-removed state. This value is used only when SpinState is UnpairedElectrons.
FPlus- Type:
Block
- Recurring:
False
- Description:
Settings for the electron-added state used to calculate Fukui f+ and the vertical electron affinity.
ChargeDelta- Type:
Integer
- Default value:
-1
- Value Range:
value < 0
- Description:
Change in total charge relative to the reference system. A value of -1 corresponds to adding one electron.
SpinState- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, Low, High, UnpairedElectrons]
- Description:
Spin state of the electron-added system. Auto currently selects the lowest spin compatible with the reference spin and charge change. Low and High explicitly select the lowest or highest directly reachable spin, respectively. UnpairedElectrons uses the value of the UnpairedElectrons keyword.
UnpairedElectrons- Type:
Integer
- Default value:
0
- Value Range:
value >= 0
- Description:
Number of unpaired electrons in the electron-added state. This value is used only when SpinState is UnpairedElectrons.
MACE- Type:
Block
- Recurring:
False
- Description:
Options for the MACE machine learning potential backend.
DataType- Type:
Multiple Choice
- Default value:
float32
- Options:
[float32, float64]
- Description:
Using
float32is faster but less accurate, and generally recommended for MD. Conversely usingfloat64is slower but more accurate, and recommended for geometry optimization.
EnableCuEquivariance- Type:
Bool
- Default value:
Yes
- Description:
Enable CUDA-accelerated cuEquivariance library for equivariant neural networks, if CUDA available.
ModelHead- Type:
String
- Default value:
- Description:
Model head to use if a custom MACE model is supplied via a parameter file (e.g. ‘omat_pbe’, ‘omol’, ‘spice_wB97M’, ‘ωB97M-D3(BJ)’, ‘rgd1_b3lyp’, ‘oc20_usemppbe’, ‘matpes_r2scan’). Ignored if a specific MACE model is selected.
MLDistanceUnit- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, angstrom, bohr]
- GUI name:
Internal distance unit
- Description:
Unit of distances expected by the ML backend (not the ASE calculator). The ASE calculator may require this information.
MLEnergyUnit- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, Hartree, eV, kcal/mol, kJ/mol]
- GUI name:
Internal energy unit
- Description:
Unit of energy output by the ML backend (not the unit output by the ASE calculator). The ASE calculator may require this information.
Model- Type:
Multiple Choice
- Default value:
ANI-2x
- Options:
[Custom, AIMNet2-B973c, AIMNet2-NSE, AIMNet2-Pd, AIMNet2-Rxn, AIMNet2-wB97MD3, ANI-1ccx, ANI-1x, ANI-2x, eSEN-S-Con-OMol, M3GNet-UP-2022, MACE-MP-0-Large, MACE-MP-0-Medium, MACE-MP-0-Small, MACE-MPA-0, NEP89, QET-MatQ, QET-PBE-2025, QET-r2SCAN-2025, TensorNet-PBE-M-2025, TensorNet-r2SCAN-M-2025, UMA-S-1.2-OC20, UMA-S-1.2-OC22, UMA-S-1.2-OC25, UMA-S-1.2-ODAC, UMA-S-1.2-OMat, UMA-S-1.2-OMC, UMA-S-1.2-OMol]
- Description:
Select a pre-parameterized or custom model.
AIMNet2 variants: best for fast calculations of small, drug-like molecules; limited to systems of 14 elements (H, B, C, N, O, F, Si, P, S, Cl, As/Pd, Se, Br, I).
ANI-(1x/1ccx/2x): best for very fast calculations of organic molecules; limited to elements H, C, N, O (ANI-1x/1ccx), F, S, Cl (ANI-2x).
eSEN-S-Con-OMol: best for highly accurate calculations of diverse organic and bio-relevant molecules; not intended for calculations on periodic inorganic bulk materials.
M3GNet-UP-2022: best for fast calculations of inorganic crystalline materials; not designed for accurately modeling small organic molecules or biomolecules.
MACE-MP-0-(Small/Medium/Large): best for accurate periodic calculations of inorganic materials; size trades speed/accuracy; not designed for accurately modeling small organic molecules or biomolecules. MACE-MPA-0 has improved accuracy vs MP-0.
QET-MatQ, QET-PBE-2025, QET-r2SCAN-2025: best for charge-aware calculations of inorganic materials, ionic systems, electrolytes, and interfaces.
TensorNet-PBE-M-2025, TensorNet-r2SCAN-M-2025: best for efficient calculations of inorganic materials.
NEP89: Neuroevolution potential for neutral systems.
UMA-S-1.2 variants: best for high accuracy calculations on a broad range of systems; choose from OC20 (adsorption and surface chemistry), OC22 (oxide catalysis), OC25 (electrocatalysis), ODAC (adsorption in porous frameworks), OMat (inorganic materials), OMC (organic molecular crystals), OMol (molecules, biomolecules, metal complexes, electrolytes); can be computationally expensive compared to other, more targeted models.
Set Custom to choose a backend and provide your own parameters.
NumThreads- Type:
String
- Default value:
- GUI name:
Number of threads
- Description:
Number of threads.
If not empty, OMP_NUM_THREADS will be set to this number; for PyTorch-engines, torch.set_num_threads() will be called.
ParameterDir- Type:
String
- Default value:
- GUI name:
Parameter directory
- Description:
Path to a set of parameters for the backend, if it expects to read from a directory.
ParameterFile- Type:
String
- Default value:
- Description:
Path to a set of parameters for the backend, if it expects to read from a file.
UnpairedElectrons- Type:
Integer
- Default value:
0
- Value Range:
value >= 0
- GUI name:
Spin polarization
- Description:
The number of unpaired electrons in the system for a spin unrestricted calculation. The spin multiplicity is taken as this value plus one.
Unrestricted- Type:
Bool
- Default value:
No
- Description:
Enables spin unrestricted calculations, passing spin information to models that accept spin input.