# deepmd.fit package

## 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

neuron

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_type

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 Get the output size. Get selected type
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.

get_out_size() int[source]

Get the output size. Should be 3

get_sel_type() int[source]

Get selected type

## 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 Get the number of atomic parameters Get the number of frame parameters init_variables(fitting_net_variables) Init the fitting net variables with the given dict
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

get_numb_aparam() int[source]

Get the number of atomic parameters

get_numb_fparam() int[source]

Get the number of frame parameters

init_variables(fitting_net_variables: dict) None[source]

Init the fitting net variables with the given dict

Parameters
fitting_net_variables

The input dict which stores the fitting net variables

## 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

neuron

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_type

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.

scale

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

diag_shift

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 Get the output size. Get selected atom types
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

get_out_size() int[source]

Get the output size. Should be 9

get_sel_type() int[source]

Get selected atom types

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 Get the output size. Get selected atom types
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

get_out_size() int[source]

Get the output size. Should be 9

get_sel_type() List[int][source]

Get selected atom types

## 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]