deepmd.fit package

Submodules

deepmd.fit.dipole module

class deepmd.fit.dipole.DipoleFittingSeA[source]

Bases: object

Fit the atomic dipole with descriptor se_a

Parameters
descrpttf.Tensor

The descrptor

neuronList[int]

Number of neurons in each hidden layer of the fitting net

resnet_dtbool

Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)

sel_typeList[int]

The atom types selected to have an atomic dipole prediction. If is None, all atoms are selected.

seedint

Random seed for initializing the network parameters.

activation_functionstr

The activation function in the embedding net. Supported options are {0}

precisionstr

The precision of the embedding net parameters. Supported options are {1}

uniform_seed

Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Methods

build(input_d, rot_mat, natoms[, reuse, suffix])

Build the computational graph for fitting net

enable_mixed_precision([mixed_prec])

Reveive the mixed precision setting.

get_out_size()

Get the output size.

get_sel_type()

Get selected type

init_variables(model_file)

Init the fitting net variables with the given frozen model

build(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: Optional[bool] = None, suffix: str = '') tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters
input_d

The input descriptor

rot_mat

The rotation matrix from the descriptor.

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

reuse

The weights in the networks should be reused when get the variable.

suffix

Name suffix to identify this descriptor

Returns
dipole

The atomic dipole.

enable_mixed_precision(mixed_prec: Optional[dict] = None) None[source]

Reveive the mixed precision setting.

Parameters
mixed_prec

The mixed precision setting used in the embedding net

get_out_size() int[source]

Get the output size. Should be 3

get_sel_type() int[source]

Get selected type

init_variables(model_file: str) None[source]

Init the fitting net variables with the given frozen model

Parameters
model_filestr

The input frozen model file

deepmd.fit.ener module

class deepmd.fit.ener.EnerFitting[source]

Bases: object

Fitting the energy of the system. The force and the virial can also be trained.

The potential energy \(E\) is a fitting network function of the descriptor \(\mathcal{D}\):

\[E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} \circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}\]

The first \(n\) hidden layers \(\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}\) are given by

\[\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})\]

where \(\mathbf{x} \in \mathbb{R}^{N_1}\) is the input vector and \(\mathbf{y} \in \mathbb{R}^{N_2}\) is the output vector. \(\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}\) and \(\mathbf{b} \in \mathbb{R}^{N_2}\) are weights and biases, respectively, both of which are trainable if trainable[i] is True. \(\boldsymbol{\phi}\) is the activation function.

The output layer \(\mathcal{L}^{(n)}\) is given by

\[\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})= \mathbf{x}^T\mathbf{w}+\mathbf{b}\]

where \(\mathbf{x} \in \mathbb{R}^{N_{n-1}}\) is the input vector and \(\mathbf{y} \in \mathbb{R}\) is the output scalar. \(\mathbf{w} \in \mathbb{R}^{N_{n-1}}\) and \(\mathbf{b} \in \mathbb{R}\) are weights and bias, respectively, both of which are trainable if trainable[n] is True.

Parameters
descrpt

The descrptor \(\mathcal{D}\)

neuron

Number of neurons \(N\) in each hidden layer of the fitting net

resnet_dt

Time-step dt in the resnet construction: \(y = x + dt * \phi (Wx + b)\)

numb_fparam

Number of frame parameter

numb_aparam

Number of atomic parameter

rcond

The condition number for the regression of atomic energy.

tot_ener_zero

Force the total energy to zero. Useful for the charge fitting.

trainable

If the weights of fitting net are trainable. Suppose that we have \(N_l\) hidden layers in the fitting net, this list is of length \(N_l + 1\), specifying if the hidden layers and the output layer are trainable.

seed

Random seed for initializing the network parameters.

atom_ener

Specifying atomic energy contribution in vacuum. The set_davg_zero key in the descrptor should be set.

activation_function

The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are {0}

precision

The precision of the embedding net parameters. Supported options are {1}

uniform_seed

Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Methods

build(inputs, natoms[, input_dict, reuse, ...])

Build the computational graph for fitting net

compute_input_stats(all_stat[, protection])

Compute the input statistics

compute_output_stats(all_stat)

Compute the ouput statistics

enable_compression(model_file[, suffix])

Set the fitting net attributes from the frozen model_file when fparam or aparam is not zero

enable_mixed_precision([mixed_prec])

Reveive the mixed precision setting.

get_numb_aparam()

Get the number of atomic parameters

get_numb_fparam()

Get the number of frame parameters

init_variables(model_file)

Init the fitting net variables with the given frozen model

build(inputs: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, input_dict: dict = {}, reuse: Optional[bool] = None, suffix: str = '') tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters
inputs

The input descriptor

input_dict

Additional dict for inputs. if numb_fparam > 0, should have input_dict[‘fparam’] if numb_aparam > 0, should have input_dict[‘aparam’]

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

reuse

The weights in the networks should be reused when get the variable.

suffix

Name suffix to identify this descriptor

Returns
ener

The system energy

compute_input_stats(all_stat: dict, protection: float = 0.01) None[source]

Compute the input statistics

Parameters
all_stat

if numb_fparam > 0 must have all_stat[‘fparam’] if numb_aparam > 0 must have all_stat[‘aparam’] can be prepared by model.make_stat_input

protection

Divided-by-zero protection

compute_output_stats(all_stat: dict) None[source]

Compute the ouput statistics

Parameters
all_stat

must have the following components: all_stat[‘energy’] of shape n_sys x n_batch x n_frame can be prepared by model.make_stat_input

enable_compression(model_file: str, suffix: str = '') None[source]

Set the fitting net attributes from the frozen model_file when fparam or aparam is not zero

Parameters
model_filestr

The input frozen model file

suffixstr, optional

The suffix of the scope

enable_mixed_precision(mixed_prec: Optional[dict] = None) None[source]

Reveive the mixed precision setting.

Parameters
mixed_prec

The mixed precision setting used in the embedding net

get_numb_aparam() int[source]

Get the number of atomic parameters

get_numb_fparam() int[source]

Get the number of frame parameters

init_variables(model_file: str) None[source]

Init the fitting net variables with the given frozen model

Parameters
model_filestr

The input frozen model file

deepmd.fit.polar module

class deepmd.fit.polar.GlobalPolarFittingSeA[source]

Bases: object

Fit the system polarizability with descriptor se_a

Parameters
descrpttf.Tensor

The descrptor

neuronList[int]

Number of neurons in each hidden layer of the fitting net

resnet_dtbool

Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)

sel_typeList[int]

The atom types selected to have an atomic polarizability prediction

fit_diagbool

Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.

scaleList[float]

The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]

diag_shiftList[float]

The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.

seedint

Random seed for initializing the network parameters.

activation_functionstr

The activation function in the embedding net. Supported options are {0}

precisionstr

The precision of the embedding net parameters. Supported options are {1}

Methods

build(input_d, rot_mat, natoms[, reuse, suffix])

Build the computational graph for fitting net

enable_mixed_precision([mixed_prec])

Reveive the mixed precision setting.

get_out_size()

Get the output size.

get_sel_type()

Get selected atom types

init_variables(model_file)

Init the fitting net variables with the given frozen model

build(input_d, rot_mat, natoms, reuse=None, suffix='') tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters
input_d

The input descriptor

rot_mat

The rotation matrix from the descriptor.

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

reuse

The weights in the networks should be reused when get the variable.

suffix

Name suffix to identify this descriptor

Returns
polar

The system polarizability

enable_mixed_precision(mixed_prec: Optional[dict] = None) None[source]

Reveive the mixed precision setting.

Parameters
mixed_prec

The mixed precision setting used in the embedding net

get_out_size() int[source]

Get the output size. Should be 9

get_sel_type() int[source]

Get selected atom types

init_variables(model_file: str) None[source]

Init the fitting net variables with the given frozen model

Parameters
model_filestr

The input frozen model file

class deepmd.fit.polar.PolarFittingLocFrame(jdata, descrpt)[source]

Bases: object

Fitting polarizability with local frame descriptor.

Deprecated since version 2.0.0: This class is not supported any more.

Methods

build

get_out_size

get_sel_type

build(input_d, rot_mat, natoms, reuse=None, suffix='')[source]
get_out_size()[source]
get_sel_type()[source]
class deepmd.fit.polar.PolarFittingSeA[source]

Bases: object

Fit the atomic polarizability with descriptor se_a

Methods

build(input_d, rot_mat, natoms[, reuse, suffix])

Build the computational graph for fitting net

compute_input_stats(all_stat[, protection])

Compute the input statistics

enable_mixed_precision([mixed_prec])

Reveive the mixed precision setting.

get_out_size()

Get the output size.

get_sel_type()

Get selected atom types

init_variables(model_file)

Init the fitting net variables with the given frozen model

build(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: Optional[bool] = None, suffix: str = '')[source]

Build the computational graph for fitting net

Parameters
input_d

The input descriptor

rot_mat

The rotation matrix from the descriptor.

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

reuse

The weights in the networks should be reused when get the variable.

suffix

Name suffix to identify this descriptor

Returns
atomic_polar

The atomic polarizability

compute_input_stats(all_stat, protection=0.01)[source]

Compute the input statistics

Parameters
all_stat

Dictionary of inputs. can be prepared by model.make_stat_input

protection

Divided-by-zero protection

enable_mixed_precision(mixed_prec: Optional[dict] = None) None[source]

Reveive the mixed precision setting.

Parameters
mixed_prec

The mixed precision setting used in the embedding net

get_out_size() int[source]

Get the output size. Should be 9

get_sel_type() List[int][source]

Get selected atom types

init_variables(model_file: str) None[source]

Init the fitting net variables with the given frozen model

Parameters
model_filestr

The input frozen model file

deepmd.fit.wfc module

class deepmd.fit.wfc.WFCFitting(jdata, descrpt)[source]

Bases: object

Fitting Wannier function centers (WFCs) with local frame descriptor.

Deprecated since version 2.0.0: This class is not supported any more.

Methods

build

get_out_size

get_sel_type

get_wfc_numb

build(input_d, rot_mat, natoms, reuse=None, suffix='')[source]
get_out_size()[source]
get_sel_type()[source]
get_wfc_numb()[source]