kliff.calculators#
- class kliff.calculators.Calculator(model)[source]#
Calculator to compute properties (e.g. energy and forces) using a potential model for each configuration in the dataset.
The properties, together with the corresponding reference data stored in the configurations, are provided to
Loss
to construct a loss function for the optimizer.In the reverse direction, a calculator grabs the new parameters from the optimizer and update the model with the new parameters.
- create(configs, use_energy=True, use_forces=True, use_stress=False)[source]#
Create compute arguments for a collection of configurations.
By compute arguments, we mean the information needed by a model to carry on a calculation, such as the coordinates, species, cutoff distance, neighbor list, etc. Each configuration has its own compute arguments, and this function creates the compute arguments for all the configurations in
configs
.- Parameters
configs (
List
[Configuration
]) – atomic configurations. instances.use_energy (
Union
[List
[bool
],bool
]) – Whether to require the calculator to compute energy. If a list of bool is provided, each component is for one configuration in configs. If a bool is provided, it is applied to all configurations.use_forces (
Union
[List
[bool
],bool
]) – Whether to require the calculator to compute forces. If a list of bool is provided, each component is for one configuration in configs. If a bool is provided, it is applied to all configurations.use_stress (
Union
[List
[bool
],bool
]) – Whether to require the calculator to compute stress. If a list of bool is provided, each component is for one configuration in configs. If a bool is provided, it is applied to all configurations.
- get_compute_arguments()[source]#
Return a list of compute arguments, each associated with a configuration.
- Return type
List
[Any
]
- compute(compute_arguments)[source]#
Compute the properties given the compute arguments associated with a configuration.
- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Return type
Dict
[str
,Any
]- Returns
- A dictionary of properties, with keys of energy, forces and stress,
values of float or np.array.
- get_energy(compute_arguments)[source]#
Get the energy of a configuration.
- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Return type
float
- Returns
The energy of the configuration associated with the compute arguments.
- get_forces(compute_arguments)[source]#
Get the forces of a configuration.
- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Return type
array
- Returns
- Forces on atoms. 2D array of shape (N, 3), where N is the number of atoms
in the configuration.
- get_stress(compute_arguments)[source]#
Get the stress of a configuration.
- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Return type
array
- Returns
Virial stress of the configuration associated with the compute arguments, 1D array with 6 component. The returned stress is in Voigt notation, i.e. this function returns: []
- get_prediction(compute_arguments)[source]#
Get the prediction of all properties that are requested to compute.
The energy, forces, and stress are each flattened to a 1D array, and then concatenated (in the order of energy, forces, and stress) to form the prediction. Depending on the values of
use_energy
,use_forces
, anduse_stress
that are provided increate()
, one or more of energy, forces and stress may not be included in the prediction.- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Returns
If
use_energy
,use_forces
, anduse_stress
are allTrue
, the size of prediction is 1+3N+6, with the 1st component the energy, the 2nd to 1+3N components the flattened forces on N atoms, and the remaining 6 components the Voigt stress.If one or more of
use_energy
,use_forces
, anduse_stress
isFalse
, its corresponding value is removed from prediction, and the size of prediction shrinks accordingly. For example, if bothuse_energy
anduse_stress
areTrue
butuse_forces
isFalse
, then the size of prediction is 1+6, with the 1st component the energy, and the 2nd to 7th components the Voigt stress.
- Return type
1D array of predictions. For a configuration of N atoms
- get_reference(compute_arguments)[source]#
Get the reference data of all properties that are requested to compute.
Same as
get_prediction()
, the energy, forces, and stress are each flattened to a 1D array, and then concatenated (in the order of energy, forces, and stress) to form the reference. Depending on the values ofuse_energy
,use_forces
, anduse_stress
that are provided increate()
, one or more of energy, forces and stress may not be included in the reference.- Parameters
compute_arguments – A compute arguments instance for a configuration.
- Returns
If
use_energy
,use_forces
, anduse_stress
are allTrue
, the size of reference is 1+3N+6, with the 1st component the energy, the 2nd to 1+3N components the flattened forces on N atoms, and the remaining 6 components the Voigt stress.If one or more of
use_energy
,use_forces
, anduse_stress
isFalse
, its corresponding value is removed from reference, and the size of reference shrinks accordingly. For example, if bothuse_energy
anduse_stress
areTrue
butuse_forces
isFalse
, then the size of reference is 1+6, with the 1st component the energy, and the 2nd to 7th components the Voigt stress.
- Return type
1D array of reference values. For a configuration of N atoms
- get_opt_params()[source]#
Return a list of optimizing parameters.
The optimizing parameters is a list consisting of the values of the model parameters that is set to fit via
kliff.models.Model.set_opt_params()
orkliff.models.Model.set_one_opt_param()
. The returned value is typically passed to an optimizer as the initial values to carry out the optimization.- Return type
array
- has_opt_params_bounds()[source]#
Return a bool to indicate whether there are parameters whose bounds are provided.
- get_opt_params_bounds()[source]#
Return the lower and upper bounds for the optimizing parameters.
The returned value is a list of (lower, upper) tuples. Each tuple contains the lower and upper bounds for the corresponding parameter obtained from
get_opt_params()
.None
forlower
orupper
means that no bound should be applied.
- update_model_params(opt_params)[source]#
Update model parameters from a list of opt_params.
This is the reverse of
get_opt_params()
.- Parameters
opt_params (
List
[float
]) – Optimizing parameters.
- class kliff.calculators.CalculatorTorch(model, gpu=None)[source]#
A calculator for torch based models.
- Parameters
model (
ModelTorch
) – torch models, e.g.NeuralNetwork
.gpu (
Union
[bool
,int
,None
]) – whether to use gpu for training. If int (e.g. 0), will trained on this gpu device. If True will always train on gpu 0.
- implemented_property = ['energy', 'forces', 'stress']#
- create(configs, use_energy=True, use_forces=True, use_stress=False, fingerprints_filename='fingerprints.pkl', fingerprints_mean_stdev_filename=None, reuse=False, use_welford_method=False, nprocs=1)[source]#
Process configs to generate fingerprints.
- Parameters
configs (
List
[Configuration
]) – atomic configurationsuse_energy (
bool
) – Whether to require the calculator to compute energy.use_forces (
bool
) – Whether to require the calculator to compute forces.use_stress (
bool
) – Whether to require the calculator to compute stress.fingerprints_filename (
Union
[Path
,str
]) – Path to save the generated fingerprints. If reuse=True, Will not generate the fingerprints, but directly use the one provided via this file.fingerprints_mean_stdev_filename (
Union
[str
,Path
,None
]) – Path to save the mean and standard deviation of the fingerprints. If reuse=True, Will not generate new fingerprints mean and stdev, but directly use the one provided via this file. If normalize is not required by a descriptor, this is ignored.reuse (
bool
) – Whether to reuse provided fingerprints.use_welford_method (
bool
) – Whether to compute mean and standard deviation using the Welford method, which is memory efficient. See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variancenprocs (
int
) – Number of processes used to generate the fingerprints. If 1, run in serial mode, otherwise nprocs processes will be forked via multiprocessing to do the work.