MLPotential Keywords¶

Engine MLPotential¶

Backend

Type:

Multiple Choice

Options:

[M3GNet, NequIP, MACE, SchNetPack, sGDML, TorchANI]

Description:

The machine learning potential backend.

Device

Type:

Multiple Choice

Default value:

Options:

[, cpu, cuda:0, cuda:1, mps]

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.

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:

No

Description:

Enable CUDA-accelerated cuEquivariance library for equivariant neural networks (requires CUDA).

Version

Type:

Multiple Choice

Default value:

Latest

Options:

[Latest, Latest-CPU, Latest-CU128]

Description:

Version of environment to use. Latest [default] uses any available installed version. Latest CPU uses the CPU-only version. Latest CU128 uses the CUDA 12.8 version.

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, MACE-MP-0-large, MACE-MP-0-medium, MACE-MP-0-small, M3GNet-UP-2022]

Description:

Select a particular parameterization.

ANI-1x and ANI-2x: based on DFT (wB97X)

ANI-1cxx: based on DLPNO-CCSD(T)/CBS

MACE-MP-0: based on DFT (PBE+U) data.

M3GNet-UP-2022: based on DFT (PBE and PBE+U) data.

AIMNet2: based on ωB97m-D3 or B97-3c data.

ANI-1x and ANI-1ccx have been parameterized to give good geometries, vibrational frequencies, and reaction energies for gasphase organic molecules containing H, C, O, and N. ANI-2x can also handle the atoms F, S, and Cl.

MACE-MP-0 is a pre-trained foundation model for materials chemistry, parameterised for 89 chemical elements. It is available in three sizes (small/medium/large) which balance accuracy vs. compute.

M3GNet-UP-2022 is a universal potential (UP) for the entire periodic table and has been primarily trained to crystal data (energies, forces, stresses) from the Materials Project.

AIMNet2 has been parametrized to give good geometries and reaction energies for gasphase molecules and ions containing H, B, C, N, O, F, Si, P, S, Cl, As, Se, Br, I.

Set to Custom to specify the backend and parameter files yourself.

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.