MLPotential Keywords¶
Engine MLPotential¶
AIMNet2- Type:
Block
- Recurring:
False
- Description:
Options for the AIMNet2 machine learning potential backend.
Version- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, v1, v1-cpu, v1-cu128]
- Description:
Version of SCM AIMNet2 environment to use. Defaults to
Autoi.e. the best match for a given model, or the latest installed version. Selecting v1 uses any available v1 environment, v1-cpu the CPU-only version, v1-cu128 the CUDA 12.8 version.
Backend- Type:
Multiple Choice
- Options:
[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.
M3GNet- Type:
Block
- Recurring:
False
- Description:
Options for the M3GNet machine learning potential backend.
Version- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, v1]
- Description:
Version of SCM M3GNet environment to use. Defaults to
Autoi.e. the best match for a given model, or the latest installed version. Selecting v1 uses the available v1 environment.
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:
No
- Description:
Enable CUDA-accelerated cuEquivariance library for equivariant neural networks (requires CUDA).
Version- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, v1, v1-cpu, v1-cu128]
- Description:
Version of SCM MACE environment to use. Defaults to
Autoi.e. the best match for a given model or the latest installed version. Selecting v1 uses any available v1 environment, v1-cpu the CPU-only version, v1-cu128 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, M3GNet-UP-2022, MACE-MP-0-large, MACE-MP-0-medium, MACE-MP-0-small, MACE-MPA-0]
- 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/MACE-MPA-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.
NequIP- Type:
Block
- Recurring:
False
- Description:
Options for the NequIP machine learning potential backend.
Version- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, v1, v1-cpu, v1-cu128]
- Description:
Version of SCM NequIP environment to use. Defaults to
Autoi.e. the best match for a given model or the latest installed version. Selecting v1 uses any available v1 environment, v1-cpu the CPU-only version, v1-cu128 the CUDA 12.8 version.
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.
TorchANI- Type:
Block
- Recurring:
False
- Description:
Options for the TorchANI machine learning potential backend.
Version- Type:
Multiple Choice
- Default value:
Auto
- Options:
[Auto, v1, v1-cpu, v1-cu128]
- Description:
Version of SCM TorchANI environment to use. Defaults to
Autoi.e. the best match for a given model or the latest installed version. Selecting v1 uses any available v1 environment, v1-cpu the CPU-only version, v1-cu128 the CUDA 12.8 version.