MLPotential Keywords

Engine MLPotential

Backend
Type:

Multiple Choice

Options:

[FAIRChem, M3GNet, MACE, NequIP, 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:

Multiple Choice

Default value:

None

Options:

[None, OC20, OMat, OMol, ODAC, OMC]

Description:

Model task to use if a custom UMA/eSEN model is supplied via a parameter file. Ignored if a specific FAIRChem model is selected.

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 float32 is faster but less accurate, and generally recommended for MD. Conversely using float64 is 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.

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-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, UMA-S-1.1-OC20, UMA-S-1.1-ODAC, UMA-S-1.1-OMat, UMA-S-1.1-OMC, UMA-S-1.1-OMol]

Description:

Select a pre-parameterized or custom model.

AIMNet2-(wB97MD3/B973c): best for fast calculations of small, drug-like molecules; limited to aperiodic systems of 14 elements (H, B, C, N, O, F, Si, P, S, Cl, As, 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.

UMA-S-1.1 variants: best for high accuracy calculations on a broad range of systems; choose from OC20 (adsorption and surface chemistry), 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 the machine learning model. Only applicable to ‘UMA-S-1.1-OMol’ and custom FAIRChem models.