5.3. Training Parameters

Note

One can load, modify, and export the input file by using our effective web-based tool DP-GUI online or hosted using the command line interface dp gui. All training parameters below can be set in DP-GUI. By clicking “SAVE JSON”, one can download the input file for furthur training.

model:
type: dict
argument path: model
type_map:
type: typing.List[str], optional
argument path: model/type_map

A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment. If not given, type.raw in each system should use the same type indexes, and type_map.raw will take no effect.

data_stat_nbatch:
type: int, optional, default: 10
argument path: model/data_stat_nbatch

The model determines the normalization from the statistics of the data. This key specifies the number of frames in each system used for statistics.

data_stat_protect:
type: float, optional, default: 0.01
argument path: model/data_stat_protect

Protect parameter for atomic energy regression.

data_bias_nsample:
type: int, optional, default: 10
argument path: model/data_bias_nsample

The number of training samples in a system to compute and change the energy bias.

use_srtab:
type: str, optional
argument path: model/use_srtab

(Supported Backend: TensorFlow) The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.

smin_alpha:
type: float, optional
argument path: model/smin_alpha

(Supported Backend: TensorFlow) The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.

sw_rmin:
type: float, optional
argument path: model/sw_rmin

(Supported Backend: TensorFlow) The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

sw_rmax:
type: float, optional
argument path: model/sw_rmax

(Supported Backend: TensorFlow) The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

pair_exclude_types:
type: list, optional, default: []
argument path: model/pair_exclude_types

(Supported Backend: PyTorch) The atom pairs of the listed types are not treated to be neighbors, i.e. they do not see each other.

atom_exclude_types:
type: list, optional, default: []
argument path: model/atom_exclude_types

(Supported Backend: PyTorch) Exclude the atomic contribution of the listed atom types

srtab_add_bias:
type: bool, optional, default: True
argument path: model/srtab_add_bias

(Supported Backend: TensorFlow) Whether add energy bias from the statistics of the data to short-range tabulated atomic energy. It only takes effect when use_srtab is provided.

type_embedding:
type: dict, optional
argument path: model/type_embedding

(Supported Backend: TensorFlow) The type embedding.

neuron:
type: typing.List[int], optional, default: [8]
argument path: model/type_embedding/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model/type_embedding/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model/type_embedding/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:
type: str, optional, default: default
argument path: model/type_embedding/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model/type_embedding/trainable

If the parameters in the embedding net are trainable

seed:
type: int | NoneType, optional, default: None
argument path: model/type_embedding/seed

Random seed for parameter initialization

modifier:
type: dict, optional
argument path: model/modifier

(Supported Backend: TensorFlow) The modifier of model output.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key)
argument path: model/modifier/type
possible choices: dipole_charge

The type of modifier. See explanation below.

-dipole_charge: Use WFCC to model the electronic structure of the system. Correct the long-range interaction

When type is set to dipole_charge:

model_name:
type: str
argument path: model/modifier[dipole_charge]/model_name

The name of the frozen dipole model file.

model_charge_map:
type: typing.List[float]
argument path: model/modifier[dipole_charge]/model_charge_map

The charge of the WFCC. The list length should be the same as the `sel_type <model/fitting_net[dipole]/sel_type_>`_.

sys_charge_map:
type: typing.List[float]
argument path: model/modifier[dipole_charge]/sys_charge_map

The charge of real atoms. The list length should be the same as the type_map

ewald_beta:
type: float, optional, default: 0.4
argument path: model/modifier[dipole_charge]/ewald_beta

The splitting parameter of Ewald sum. Unit is A^-1

ewald_h:
type: float, optional, default: 1.0
argument path: model/modifier[dipole_charge]/ewald_h

The grid spacing of the FFT grid. Unit is A

compress:
type: dict, optional
argument path: model/compress

(Supported Backend: TensorFlow) Model compression configurations

spin:
type: dict, optional
argument path: model/spin

The settings for systems with spin.

use_spin:
type: typing.List[int] | typing.List[bool]
argument path: model/spin/use_spin

Whether to use atomic spin model for each atom type. List of boolean values with the shape of [ntypes] to specify which types use spin, or a list of integer values (Supported Backend: PyTorch) to indicate the index of the type that uses spin.

spin_norm:
type: typing.List[float], optional
argument path: model/spin/spin_norm

(Supported Backend: TensorFlow) The magnitude of atomic spin for each atom type with spin

virtual_len:
type: typing.List[float], optional
argument path: model/spin/virtual_len

(Supported Backend: TensorFlow) The distance between virtual atom representing spin and its corresponding real atom for each atom type with spin

virtual_scale:
type: float | typing.List[float], optional
argument path: model/spin/virtual_scale

(Supported Backend: PyTorch) The scaling factor to determine the virtual distance between a virtual atom representing spin and its corresponding real atom for each atom type with spin. This factor is defined as the virtual distance divided by the magnitude of atomic spin for each atom type with spin. The virtual coordinate is defined as the real coordinate plus spin * virtual_scale. List of float values with shape of [ntypes] or [ntypes_spin] or one single float value for all types, only used when use_spin is True for each atom type.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key), default: standard
argument path: model/type

When type is set to standard:

Stardard model, which contains a descriptor and a fitting.

descriptor:
type: dict
argument path: model[standard]/descriptor

The descriptor of atomic environment.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key)
argument path: model[standard]/descriptor/type

The type of the descritpor. See explanation below.

  • loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

  • se_e2_a: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.

  • se_e2_r: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.

  • se_e3: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.

  • se_a_tpe: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.

  • se_atten: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.

  • se_atten_v2: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism with new modifications will be used by this descriptor.

  • se_a_mask: Used by the smooth edition of Deep Potential. It can accept a variable number of atoms in a frame (Non-PBC system). aparam are required as an indicator matrix for the real/virtual sign of input atoms.

  • hybrid: Concatenate of a list of descriptors as a new descriptor.

When type is set to loc_frame:

(Supported Backend: TensorFlow)

sel_a:
type: typing.List[int]
argument path: model[standard]/descriptor[loc_frame]/sel_a

A list of integers. The length of the list should be the same as the number of atom types in the system. sel_a[i] gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor.

sel_r:
type: typing.List[int]
argument path: model[standard]/descriptor[loc_frame]/sel_r

A list of integers. The length of the list should be the same as the number of atom types in the system. sel_r[i] gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[loc_frame]/rcut

The cut-off radius. The default value is 6.0

axis_rule:
type: typing.List[int]
argument path: model[standard]/descriptor[loc_frame]/axis_rule

A list of integers. The length should be 6 times of the number of types.

  • axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

  • axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.

  • axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.

  • axis_rule[i*6+3]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

  • axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.

  • axis_rule[i*6+5]: index of the axis atom defining the second axis. Note that the neighbors with the same class and type are sorted according to their relative distance.

When type is set to se_e2_a (or its alias se_a):

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_e2_a]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_e2_a]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_e2_a]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_e2_a]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_e2_a]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_e2_a]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_a]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_a]/type_one_side

If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_e2_a]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_e2_a]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_e2_a]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_e2_a]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

env_protection:
type: float, optional, default: 0.0
argument path: model[standard]/descriptor[se_e2_a]/env_protection

(Supported Backend: TensorFlow) Protection parameter to prevent division by zero errors during environment matrix calculations. For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection.

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_a]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to se_e3 (or its aliases se_at, se_a_3be, se_t):

(Supported Backend: TensorFlow)

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_e3]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_e3]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_e3]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_e3]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_e3]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e3]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_e3]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_e3]/trainable

If the parameters in the embedding net are trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_e3]/seed

Random seed for parameter initialization

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e3]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to se_a_tpe (or its alias se_a_ebd):

(Supported Backend: TensorFlow)

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_a_tpe]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_a_tpe]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_a_tpe]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_a_tpe]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_a_tpe]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_a_tpe]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_tpe]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_tpe]/type_one_side

If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_a_tpe]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_a_tpe]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_a_tpe]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_a_tpe]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

env_protection:
type: float, optional, default: 0.0
argument path: model[standard]/descriptor[se_a_tpe]/env_protection

(Supported Backend: TensorFlow) Protection parameter to prevent division by zero errors during environment matrix calculations. For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection.

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_tpe]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

type_nchanl:
type: int, optional, default: 4
argument path: model[standard]/descriptor[se_a_tpe]/type_nchanl

number of channels for type embedding

type_nlayer:
type: int, optional, default: 2
argument path: model[standard]/descriptor[se_a_tpe]/type_nlayer

number of hidden layers of type embedding net

numb_aparam:
type: int, optional, default: 0
argument path: model[standard]/descriptor[se_a_tpe]/numb_aparam

dimension of atomic parameter. if set to a value > 0, the atomic parameters are embedded.

When type is set to se_e2_r (or its alias se_r):

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_e2_r]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_e2_r]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_e2_r]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_e2_r]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_e2_r]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_r]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_r]/type_one_side

If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_e2_r]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_e2_r]/trainable

If the parameters in the embedding net are trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_e2_r]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_e2_r]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_e2_r]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to hybrid:

list:
type: list
argument path: model[standard]/descriptor[hybrid]/list

A list of descriptor definitions

When type is set to se_atten (or its alias dpa1):

sel:
type: str | int | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_atten]/sel

This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:

  • int. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200.

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. Only the summation of sel[i] matters, and it is recommended to be less than 200. - str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_atten]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_atten]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_atten]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_atten]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_atten]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/resnet_dt

(Supported Backend: TensorFlow) Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/type_one_side

(Supported Backend: TensorFlow) If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_atten]/precision

(Supported Backend: TensorFlow) The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/trainable

(Supported Backend: TensorFlow) If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_atten]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_atten]/exclude_types

(Supported Backend: TensorFlow) The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

attn:
type: int, optional, default: 128
argument path: model[standard]/descriptor[se_atten]/attn

The length of hidden vectors in attention layers

attn_layer:
type: int, optional, default: 2
argument path: model[standard]/descriptor[se_atten]/attn_layer

The number of attention layers. Note that model compression of se_atten is only enabled when attn_layer==0 and stripped_type_embedding is True

attn_dotr:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/attn_dotr

Whether to do dot product with the normalized relative coordinates

attn_mask:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/attn_mask

Whether to do mask on the diagonal in the attention matrix

stripped_type_embedding:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/stripped_type_embedding

(Supported Backend: TensorFlow) Whether to strip the type embedding into a separated embedding network. Setting it to False will fall back to the previous version of se_atten which is non-compressible.

smooth_type_embdding:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/smooth_type_embdding

(Supported Backend: TensorFlow) When using stripped type embedding, whether to dot smooth factor on the network output of type embedding to keep the network smooth, instead of setting set_davg_zero to be True.

set_davg_zero:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/set_davg_zero

Set the normalization average to zero. This option should be set when se_atten descriptor or atom_ener in the energy fitting is used

tebd_dim:
type: int, optional, default: 8
argument path: model[standard]/descriptor[se_atten]/tebd_dim

(Supported Backend: PyTorch) The dimension of atom type embedding.

tebd_input_mode:
type: str, optional, default: concat
argument path: model[standard]/descriptor[se_atten]/tebd_input_mode

(Supported Backend: PyTorch) This feature will be removed in a future release.

post_ln:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/post_ln

(Supported Backend: PyTorch) This feature will be removed in a future release.

ffn:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/ffn

(Supported Backend: PyTorch) This feature will be removed in a future release.

ffn_embed_dim:
type: int, optional, default: 1024
argument path: model[standard]/descriptor[se_atten]/ffn_embed_dim

(Supported Backend: PyTorch) This feature will be removed in a future release.

scaling_factor:
type: float, optional, default: 1.0
argument path: model[standard]/descriptor[se_atten]/scaling_factor

(Supported Backend: PyTorch) The scaling factor of normalization in calculations of attention weights, which is used to scale the matmul(Q, K). If temperature is None, the scaling of attention weights is (N_hidden_dim * scaling_factor)**0.5. Else, the scaling of attention weights is setting to temperature.

head_num:
type: int, optional, default: 1
argument path: model[standard]/descriptor[se_atten]/head_num

(Supported Backend: PyTorch) This feature will be removed in a future release.

normalize:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/normalize

(Supported Backend: PyTorch) Whether to normalize the hidden vectors during attention calculation.

temperature:
type: float, optional
argument path: model[standard]/descriptor[se_atten]/temperature

(Supported Backend: PyTorch) The scaling factor of normalization in calculations of attention weights, which is used to scale the matmul(Q, K).

return_rot:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten]/return_rot

(Supported Backend: PyTorch) This feature will be removed in a future release.

concat_output_tebd:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten]/concat_output_tebd

(Supported Backend: PyTorch) Whether to concat type embedding at the output of the descriptor.

When type is set to se_atten_v2:

(Supported Backend: TensorFlow)

sel:
type: str | int | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_atten_v2]/sel

This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:

  • int. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200.

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. Only the summation of sel[i] matters, and it is recommended to be less than 200. - str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_atten_v2]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_atten_v2]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_atten_v2]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_atten_v2]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_atten_v2]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten_v2]/resnet_dt

(Supported Backend: TensorFlow) Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten_v2]/type_one_side

(Supported Backend: TensorFlow) If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_atten_v2]/precision

(Supported Backend: TensorFlow) The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten_v2]/trainable

(Supported Backend: TensorFlow) If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_atten_v2]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_atten_v2]/exclude_types

(Supported Backend: TensorFlow) The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

attn:
type: int, optional, default: 128
argument path: model[standard]/descriptor[se_atten_v2]/attn

The length of hidden vectors in attention layers

attn_layer:
type: int, optional, default: 2
argument path: model[standard]/descriptor[se_atten_v2]/attn_layer

The number of attention layers. Note that model compression of se_atten is only enabled when attn_layer==0 and stripped_type_embedding is True

attn_dotr:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_atten_v2]/attn_dotr

Whether to do dot product with the normalized relative coordinates

attn_mask:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten_v2]/attn_mask

Whether to do mask on the diagonal in the attention matrix

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_atten_v2]/set_davg_zero

Set the normalization average to zero. This option should be set when se_atten descriptor or atom_ener in the energy fitting is used

When type is set to dpa2:

(Supported Backend: PyTorch)

repinit_rcut:
type: float
argument path: model[standard]/descriptor[dpa2]/repinit_rcut

The cut-off radius of the repinit block

repinit_rcut_smth:
type: float
argument path: model[standard]/descriptor[dpa2]/repinit_rcut_smth

From this position the inverse distance smoothly decays to 0 at the cut-off. Use in the repinit block.

repinit_nsel:
type: int
argument path: model[standard]/descriptor[dpa2]/repinit_nsel

Maximally possible number of neighbors for repinit block.

repformer_rcut:
type: float
argument path: model[standard]/descriptor[dpa2]/repformer_rcut

The cut-off radius of the repformer block

repformer_rcut_smth:
type: float
argument path: model[standard]/descriptor[dpa2]/repformer_rcut_smth

From this position the inverse distance smoothly decays to 0 at the cut-off. Use in the repformer block.

repformer_nsel:
type: int
argument path: model[standard]/descriptor[dpa2]/repformer_nsel

Maximally possible number of neighbors for repformer block.

tebd_dim:
type: int, optional, default: 8
argument path: model[standard]/descriptor[dpa2]/tebd_dim

The dimension of atom type embedding

concat_output_tebd:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/concat_output_tebd

Whether to concat type embedding at the output of the descriptor.

repinit_neuron:
type: list, optional, default: [25, 50, 100]
argument path: model[standard]/descriptor[dpa2]/repinit_neuron

repinit block: the number of neurons in the embedding net.

repinit_axis_neuron:
type: int, optional, default: 16
argument path: model[standard]/descriptor[dpa2]/repinit_axis_neuron

repinit block: the number of dimension of split in the symmetrization op.

repinit_set_davg_zero:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repinit_set_davg_zero
repinit_activation:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[dpa2]/repinit_activation

repinit block: the activation function in the embedding net

repformer_nlayers:
type: int, optional, default: 3
argument path: model[standard]/descriptor[dpa2]/repformer_nlayers

repformers block: the number of repformer layers

repformer_g1_dim:
type: int, optional, default: 128
argument path: model[standard]/descriptor[dpa2]/repformer_g1_dim

repformers block: the dimension of single-atom rep

repformer_g2_dim:
type: int, optional, default: 16
argument path: model[standard]/descriptor[dpa2]/repformer_g2_dim

repformers block: the dimension of invariant pair-atom rep

repformer_axis_dim:
type: int, optional, default: 4
argument path: model[standard]/descriptor[dpa2]/repformer_axis_dim

repformers block: the number of dimension of split in the symmetrization ops.

repformer_do_bn_mode:
type: str, optional, default: no
argument path: model[standard]/descriptor[dpa2]/repformer_do_bn_mode

repformers block: do batch norm in the repformer layers

repformer_bn_momentum:
type: float, optional, default: 0.1
argument path: model[standard]/descriptor[dpa2]/repformer_bn_momentum

repformers block: moment in the batch normalization

repformer_update_g1_has_conv:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g1_has_conv

repformers block: update the g1 rep with convolution term

repformer_update_g1_has_drrd:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g1_has_drrd

repformers block: update the g1 rep with the drrd term

repformer_update_g1_has_grrg:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g1_has_grrg

repformers block: update the g1 rep with the grrg term

repformer_update_g1_has_attn:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g1_has_attn

repformers block: update the g1 rep with the localized self-attention

repformer_update_g2_has_g1g1:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g2_has_g1g1

repformers block: update the g2 rep with the g1xg1 term

repformer_update_g2_has_attn:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_update_g2_has_attn

repformers block: update the g2 rep with the gated self-attention

repformer_update_h2:
type: bool, optional, default: False
argument path: model[standard]/descriptor[dpa2]/repformer_update_h2

repformers block: update the h2 rep

repformer_attn1_hidden:
type: int, optional, default: 64
argument path: model[standard]/descriptor[dpa2]/repformer_attn1_hidden

repformers block: the hidden dimension of localized self-attention

repformer_attn1_nhead:
type: int, optional, default: 4
argument path: model[standard]/descriptor[dpa2]/repformer_attn1_nhead

repformers block: the number of heads in localized self-attention

repformer_attn2_hidden:
type: int, optional, default: 16
argument path: model[standard]/descriptor[dpa2]/repformer_attn2_hidden

repformers block: the hidden dimension of gated self-attention

repformer_attn2_nhead:
type: int, optional, default: 4
argument path: model[standard]/descriptor[dpa2]/repformer_attn2_nhead

repformers block: the number of heads in gated self-attention

repformer_attn2_has_gate:
type: bool, optional, default: False
argument path: model[standard]/descriptor[dpa2]/repformer_attn2_has_gate

repformers block: has gate in the gated self-attention

repformer_activation:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[dpa2]/repformer_activation

repformers block: the activation function in the MLPs.

repformer_update_style:
type: str, optional, default: res_avg
argument path: model[standard]/descriptor[dpa2]/repformer_update_style

repformers block: style of update a rep. can be res_avg or res_incr. res_avg updates a rep u with: u = 1/sqrt{n+1} (u + u_1 + u_2 + … + u_n) res_incr updates a rep u with: u = u + 1/sqrt{n} (u_1 + u_2 + … + u_n)

repformer_set_davg_zero:
type: bool, optional, default: True
argument path: model[standard]/descriptor[dpa2]/repformer_set_davg_zero

repformers block: set the avg to zero in statistics

repformer_add_type_ebd_to_seq:
type: bool, optional, default: False
argument path: model[standard]/descriptor[dpa2]/repformer_add_type_ebd_to_seq

repformers block: concatenate the type embedding at the output

When type is set to se_a_ebd_v2 (or its alias se_a_tpe_v2):

(Supported Backend: TensorFlow)

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_a_ebd_v2]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:
type: float, optional, default: 6.0
argument path: model[standard]/descriptor[se_a_ebd_v2]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model[standard]/descriptor[se_a_ebd_v2]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_a_ebd_v2]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_a_ebd_v2]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_a_ebd_v2]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_ebd_v2]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_ebd_v2]/type_one_side

If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_a_ebd_v2]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_a_ebd_v2]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_a_ebd_v2]/seed

Random seed for parameter initialization

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_a_ebd_v2]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

env_protection:
type: float, optional, default: 0.0
argument path: model[standard]/descriptor[se_a_ebd_v2]/env_protection

(Supported Backend: TensorFlow) Protection parameter to prevent division by zero errors during environment matrix calculations. For example, when using paddings, there may be zero distances of neighbors, which may make division by zero error during environment matrix calculations without protection.

set_davg_zero:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_ebd_v2]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to se_a_mask:

(Supported Backend: TensorFlow)

sel:
type: str | typing.List[int], optional, default: auto
argument path: model[standard]/descriptor[se_a_mask]/sel

This parameter sets the number of selected neighbors for each type of atom. It can be:

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

  • str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

neuron:
type: typing.List[int], optional, default: [10, 20, 40]
argument path: model[standard]/descriptor[se_a_mask]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
argument path: model[standard]/descriptor[se_a_mask]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/descriptor[se_a_mask]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_mask]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:
type: bool, optional, default: False
argument path: model[standard]/descriptor[se_a_mask]/type_one_side

If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_text{types}^2$ sets of embedding network parameters.

exclude_types:
type: typing.List[typing.List[int]], optional, default: []
argument path: model[standard]/descriptor[se_a_mask]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

precision:
type: str, optional, default: default
argument path: model[standard]/descriptor[se_a_mask]/precision

The precision of the embedding net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

trainable:
type: bool, optional, default: True
argument path: model[standard]/descriptor[se_a_mask]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model[standard]/descriptor[se_a_mask]/seed

Random seed for parameter initialization

fitting_net:
type: dict
argument path: model[standard]/fitting_net

The fitting of physical properties.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key), default: ener
argument path: model[standard]/fitting_net/type
possible choices: ener, dos, polar, dipole

The type of the fitting. See explanation below.

  • ener: Fit an energy model (potential energy surface).

  • dos : Fit a density of states model. The total density of states / site-projected density of states labels should be provided by dos.npy or atom_dos.npy in each data system. The file has number of frames lines and number of energy grid columns (times number of atoms in atom_dos.npy). See loss parameter.

  • dipole: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see sel_type) should be provided by dipole.npy in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See loss parameter.

  • polar: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see sel_type) should be provided by polarizability.npy in each data system. The file eith has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See loss parameter.

When type is set to ener:

numb_fparam:
type: int, optional, default: 0
argument path: model[standard]/fitting_net[ener]/numb_fparam

The dimension of the frame parameter. If set to >0, file fparam.npy should be included to provided the input fparams.

numb_aparam:
type: int, optional, default: 0
argument path: model[standard]/fitting_net[ener]/numb_aparam

The dimension of the atomic parameter. If set to >0, file aparam.npy should be included to provided the input aparams.

neuron:
type: typing.List[int], optional, default: [120, 120, 120], alias: n_neuron
argument path: model[standard]/fitting_net[ener]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/fitting_net[ener]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

precision:
type: str, optional, default: default
argument path: model[standard]/fitting_net[ener]/precision

The precision of the fitting net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

resnet_dt:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[ener]/resnet_dt

Whether to use a “Timestep” in the skip connection

trainable:
type: bool | typing.List[bool], optional, default: True
argument path: model[standard]/fitting_net[ener]/trainable

Whether the parameters in the fitting net are trainable. This option can be

  • bool: True if all parameters of the fitting net are trainable, False otherwise.

  • list of bool(Supported Backend: TensorFlow) : Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of this list should be equal to len(neuron)+1.

rcond:
type: float | NoneType, optional, default: None
argument path: model[standard]/fitting_net[ener]/rcond

The condition number used to determine the inital energy shift for each type of atoms. See rcond in numpy.linalg.lstsq() for more details.

seed:
type: int | NoneType, optional
argument path: model[standard]/fitting_net[ener]/seed

Random seed for parameter initialization of the fitting net

atom_ener:
type: typing.List[typing.Optional[float]], optional, default: []
argument path: model[standard]/fitting_net[ener]/atom_ener

Specify the atomic energy in vacuum for each type

layer_name:
type: typing.List[str], optional
argument path: model[standard]/fitting_net[ener]/layer_name

The name of the each layer. The length of this list should be equal to n_neuron + 1. If two layers, either in the same fitting or different fittings, have the same name, they will share the same neural network parameters. The shape of these layers should be the same. If null is given for a layer, parameters will not be shared.

use_aparam_as_mask:
type: bool, optional, default: False
argument path: model[standard]/fitting_net[ener]/use_aparam_as_mask

Whether to use the aparam as a mask in input.If True, the aparam will not be used in fitting net for embedding.When descrpt is se_a_mask, the aparam will be used as a mask to indicate the input atom is real/virtual. And use_aparam_as_mask should be set to True.

When type is set to dos:

(Supported Backend: TensorFlow)

numb_fparam:
type: int, optional, default: 0
argument path: model[standard]/fitting_net[dos]/numb_fparam

The dimension of the frame parameter. If set to >0, file fparam.npy should be included to provided the input fparams.

numb_aparam:
type: int, optional, default: 0
argument path: model[standard]/fitting_net[dos]/numb_aparam

The dimension of the atomic parameter. If set to >0, file aparam.npy should be included to provided the input aparams.

neuron:
type: typing.List[int], optional, default: [120, 120, 120]
argument path: model[standard]/fitting_net[dos]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/fitting_net[dos]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

precision:
type: str, optional, default: float64
argument path: model[standard]/fitting_net[dos]/precision

The precision of the fitting net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

resnet_dt:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[dos]/resnet_dt

Whether to use a “Timestep” in the skip connection

trainable:
type: bool | typing.List[bool], optional, default: True
argument path: model[standard]/fitting_net[dos]/trainable

Whether the parameters in the fitting net are trainable. This option can be

  • bool: True if all parameters of the fitting net are trainable, False otherwise.

  • list of bool: Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of tihs list should be equal to len(neuron)+1.

rcond:
type: float | NoneType, optional, default: None
argument path: model[standard]/fitting_net[dos]/rcond

The condition number used to determine the inital energy shift for each type of atoms. See rcond in numpy.linalg.lstsq() for more details.

seed:
type: int | NoneType, optional
argument path: model[standard]/fitting_net[dos]/seed

Random seed for parameter initialization of the fitting net

numb_dos:
type: int, optional, default: 300
argument path: model[standard]/fitting_net[dos]/numb_dos

The number of gridpoints on which the DOS is evaluated (NEDOS in VASP)

When type is set to polar:

neuron:
type: typing.List[int], optional, default: [120, 120, 120], alias: n_neuron
argument path: model[standard]/fitting_net[polar]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/fitting_net[polar]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[polar]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:
type: str, optional, default: default
argument path: model[standard]/fitting_net[polar]/precision

The precision of the fitting net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

fit_diag:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[polar]/fit_diag

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:
type: float | typing.List[float], optional, default: 1.0
argument path: model[standard]/fitting_net[polar]/scale

The output of the fitting net (polarizability matrix) will be scaled by scale

shift_diag:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[polar]/shift_diag

Whether to shift the diagonal of polar, which is beneficial to training. Default is true.

sel_type:
type: typing.List[int] | int | NoneType, optional, alias: pol_type
argument path: model[standard]/fitting_net[polar]/sel_type

The atom types for which the atomic polarizability will be provided. If not set, all types will be selected.(Supported Backend: TensorFlow)

seed:
type: int | NoneType, optional
argument path: model[standard]/fitting_net[polar]/seed

Random seed for parameter initialization of the fitting net

When type is set to dipole:

neuron:
type: typing.List[int], optional, default: [120, 120, 120], alias: n_neuron
argument path: model[standard]/fitting_net[dipole]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:
type: str, optional, default: tanh
argument path: model[standard]/fitting_net[dipole]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “linear”, “sigmoid”, “gelu”, “relu6”, “none”, “gelu_tf”, “tanh”, “softplus”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version. If you set “None” or “none” here, no activation function will be used.

resnet_dt:
type: bool, optional, default: True
argument path: model[standard]/fitting_net[dipole]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:
type: str, optional, default: default
argument path: model[standard]/fitting_net[dipole]/precision

The precision of the fitting net parameters, supported options are “float64”, “default”, “float32”, “float16”. Default follows the interface precision.

sel_type:
type: typing.List[int] | int | NoneType, optional, alias: dipole_type
argument path: model[standard]/fitting_net[dipole]/sel_type

The atom types for which the atomic dipole will be provided. If not set, all types will be selected.(Supported Backend: TensorFlow)

seed:
type: int | NoneType, optional
argument path: model[standard]/fitting_net[dipole]/seed

Random seed for parameter initialization of the fitting net

When type is set to multi:

(Supported Backend: TensorFlow) Multiple-task model.

descriptor:
type: dict
argument path: model[multi]/descriptor

The descriptor of atomic environment. See model[standard]/descriptor for details.

fitting_net_dict:
type: dict
argument path: model[multi]/fitting_net_dict

The dictionary of multiple fitting nets in multi-task mode. Each fitting_net_dict[fitting_key] is the single definition of fitting of physical properties with user-defined name fitting_key.

When type is set to frozen:

model_file:
type: str
argument path: model[frozen]/model_file

Path to the frozen model file.

When type is set to pairtab:

(Supported Backend: TensorFlow) Pairwise tabulation energy model.

tab_file:
type: str
argument path: model[pairtab]/tab_file

Path to the tabulation file.

rcut:
type: float
argument path: model[pairtab]/rcut

The cut-off radius.

sel:
type: str | int | typing.List[int]
argument path: model[pairtab]/sel

This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:

  • int. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200.

  • List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. Only the summation of sel[i] matters, and it is recommended to be less than 200. - str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

When type is set to pairwise_dprc:

(Supported Backend: TensorFlow)

qm_model:
type: dict
argument path: model[pairwise_dprc]/qm_model
qmmm_model:
type: dict
argument path: model[pairwise_dprc]/qmmm_model

When type is set to linear_ener:

(Supported Backend: TensorFlow)

models:
type: dict | list
argument path: model[linear_ener]/models

The sub-models.

weights:
type: list | str
argument path: model[linear_ener]/weights

If the type is list of float, a list of weights for each model. If “mean”, the weights are set to be 1 / len(models). If “sum”, the weights are set to be 1.

learning_rate:
type: dict, optional
argument path: learning_rate

The definitio of learning rate

scale_by_worker:
type: str, optional, default: linear
argument path: learning_rate/scale_by_worker

When parallel training or batch size scaled, how to alter learning rate. Valid values are linear`(default), `sqrt or none.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key), default: exp
argument path: learning_rate/type
possible choices: exp

The type of the learning rate.

When type is set to exp:

start_lr:
type: float, optional, default: 0.001
argument path: learning_rate[exp]/start_lr

The learning rate at the start of the training.

stop_lr:
type: float, optional, default: 1e-08
argument path: learning_rate[exp]/stop_lr

The desired learning rate at the end of the training. When decay_rate (Supported Backend: PyTorch) is explicitly set, this value will serve as the minimum learning rate during training. In other words, if the learning rate decays below stop_lr, stop_lr will be applied instead.

decay_steps:
type: int, optional, default: 5000
argument path: learning_rate[exp]/decay_steps

The learning rate is decaying every this number of training steps.

decay_rate:
type: float | NoneType, optional, default: None
argument path: learning_rate[exp]/decay_rate

(Supported Backend: PyTorch) The decay rate for the learning rate. If this is provided, it will be used directly as the decay rate for learning rate instead of calculating it through interpolation between start_lr and stop_lr.

learning_rate_dict:
type: dict, optional
argument path: learning_rate_dict

The dictionary of definitions of learning rates in multi-task mode. Each learning_rate_dict[fitting_key], with user-defined name fitting_key in model/fitting_net_dict, is the single definition of learning rate.

loss:
type: dict, optional
argument path: loss

The definition of loss function. The loss type should be set to tensor, ener or left unset.

Depending on the value of type, different sub args are accepted.

type:
type: str (flag key), default: ener
argument path: loss/type
possible choices: ener, ener_spin, dos, tensor

The type of the loss. When the fitting type is ener, the loss type should be set to ener or left unset. When the fitting type is dipole or polar, the loss type should be set to tensor.

When type is set to ener:

start_pref_e:
type: int | float, optional, default: 0.02
argument path: loss[ener]/start_pref_e

The prefactor of energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the energy label should be provided by file energy.npy in each data system. If both start_pref_e and limit_pref_e are set to 0, then the energy will be ignored.

limit_pref_e:
type: int | float, optional, default: 1.0
argument path: loss[ener]/limit_pref_e

The prefactor of energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_f:
type: int | float, optional, default: 1000
argument path: loss[ener]/start_pref_f

The prefactor of force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force label should be provided by file force.npy in each data system. If both start_pref_f and limit_pref_f are set to 0, then the force will be ignored.

limit_pref_f:
type: int | float, optional, default: 1.0
argument path: loss[ener]/limit_pref_f

The prefactor of force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_v:
type: int | float, optional, default: 0.0
argument path: loss[ener]/start_pref_v

The prefactor of virial loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the virial label should be provided by file virial.npy in each data system. If both start_pref_v and limit_pref_v are set to 0, then the virial will be ignored.

limit_pref_v:
type: int | float, optional, default: 0.0
argument path: loss[ener]/limit_pref_v

The prefactor of virial loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_ae:
type: int | float, optional, default: 0.0
argument path: loss[ener]/start_pref_ae

The prefactor of atomic energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_ener label should be provided by file atom_ener.npy in each data system. If both start_pref_ae and limit_pref_ae are set to 0, then the atomic energy will be ignored.

limit_pref_ae:
type: int | float, optional, default: 0.0
argument path: loss[ener]/limit_pref_ae

The prefactor of atomic energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_pf:
type: int | float, optional, default: 0.0
argument path: loss[ener]/start_pref_pf

The prefactor of atomic prefactor force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_pref label should be provided by file atom_pref.npy in each data system. If both start_pref_pf and limit_pref_pf are set to 0, then the atomic prefactor force will be ignored.

limit_pref_pf:
type: int | float, optional, default: 0.0
argument path: loss[ener]/limit_pref_pf

The prefactor of atomic prefactor force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

relative_f:
type: float | NoneType, optional
argument path: loss[ener]/relative_f

If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by relative_f, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.

enable_atom_ener_coeff:
type: bool, optional, default: False
argument path: loss[ener]/enable_atom_ener_coeff

If true, the energy will be computed as sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it’s 1.

start_pref_gf:
type: float, optional, default: 0.0
argument path: loss[ener]/start_pref_gf

The prefactor of generalized force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the drdq label should be provided by file drdq.npy in each data system. If both start_pref_gf and limit_pref_gf are set to 0, then the generalized force will be ignored.

limit_pref_gf:
type: float, optional, default: 0.0
argument path: loss[ener]/limit_pref_gf

The prefactor of generalized force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

numb_generalized_coord:
type: int, optional, default: 0
argument path: loss[ener]/numb_generalized_coord

The dimension of generalized coordinates. Required when generalized force loss is used.

When type is set to ener_spin:

start_pref_e:
type: int | float, optional, default: 0.02
argument path: loss[ener_spin]/start_pref_e

The prefactor of energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the energy label should be provided by file energy.npy in each data system. If both start_pref_energy and limit_pref_energy are set to 0, then the energy will be ignored.

limit_pref_e:
type: int | float, optional, default: 1.0
argument path: loss[ener_spin]/limit_pref_e

The prefactor of energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_fr:
type: int | float, optional, default: 1000
argument path: loss[ener_spin]/start_pref_fr

The prefactor of force_real_atom loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force_real_atom label should be provided by file force_real_atom.npy in each data system. If both start_pref_force_real_atom and limit_pref_force_real_atom are set to 0, then the force_real_atom will be ignored.

limit_pref_fr:
type: int | float, optional, default: 1.0
argument path: loss[ener_spin]/limit_pref_fr

The prefactor of force_real_atom loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_fm:
type: int | float, optional, default: 10000
argument path: loss[ener_spin]/start_pref_fm

The prefactor of force_magnetic loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force_magnetic label should be provided by file force_magnetic.npy in each data system. If both start_pref_force_magnetic and limit_pref_force_magnetic are set to 0, then the force_magnetic will be ignored.

limit_pref_fm:
type: int | float, optional, default: 10.0
argument path: loss[ener_spin]/limit_pref_fm

The prefactor of force_magnetic loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_v:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/start_pref_v

The prefactor of virial loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the virial label should be provided by file virial.npy in each data system. If both start_pref_virial and limit_pref_virial are set to 0, then the virial will be ignored.

limit_pref_v:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/limit_pref_v

The prefactor of virial loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_ae:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/start_pref_ae

The prefactor of atom_ener loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_ener label should be provided by file atom_ener.npy in each data system. If both start_pref_atom_ener and limit_pref_atom_ener are set to 0, then the atom_ener will be ignored.

limit_pref_ae:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/limit_pref_ae

The prefactor of atom_ener loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_pf:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/start_pref_pf

The prefactor of atom_pref loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_pref label should be provided by file atom_pref.npy in each data system. If both start_pref_atom_pref and limit_pref_atom_pref are set to 0, then the atom_pref will be ignored.

limit_pref_pf:
type: int | float, optional, default: 0.0
argument path: loss[ener_spin]/limit_pref_pf

The prefactor of atom_pref loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

relative_f:
type: float | NoneType, optional
argument path: loss[ener_spin]/relative_f

If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by relative_f, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.

enable_atom_ener_coeff:
type: bool, optional, default: False
argument path: loss[ener_spin]/enable_atom_ener_coeff

If true, the energy will be computed as sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it’s 1.

When type is set to dos:

(Supported Backend: TensorFlow)

start_pref_dos:
type: int | float, optional, default: 0.0
argument path: loss[dos]/start_pref_dos

The prefactor of Density of State (DOS) loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the Density of State (DOS) label should be provided by file Density of State (DOS).npy in each data system. If both start_pref_Density of State (DOS) and limit_pref_Density of State (DOS) are set to 0, then the Density of State (DOS) will be ignored.

limit_pref_dos:
type: int | float, optional, default: 0.0
argument path: loss[dos]/limit_pref_dos

The prefactor of Density of State (DOS) loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_cdf:
type: int | float, optional, default: 0.0
argument path: loss[dos]/start_pref_cdf

The prefactor of Cumulative Distribution Function (cumulative intergral of DOS) loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the Cumulative Distribution Function (cumulative intergral of DOS) label should be provided by file Cumulative Distribution Function (cumulative intergral of DOS).npy in each data system. If both start_pref_Cumulative Distribution Function (cumulative intergral of DOS) and limit_pref_Cumulative Distribution Function (cumulative intergral of DOS) are set to 0, then the Cumulative Distribution Function (cumulative intergral of DOS) will be ignored.

limit_pref_cdf:
type: int | float, optional, default: 0.0
argument path: loss[dos]/limit_pref_cdf

The prefactor of Cumulative Distribution Function (cumulative intergral of DOS) loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_ados:
type: int | float, optional, default: 1.0
argument path: loss[dos]/start_pref_ados

The prefactor of atomic DOS (site-projected DOS) loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atomic DOS (site-projected DOS) label should be provided by file atomic DOS (site-projected DOS).npy in each data system. If both start_pref_atomic DOS (site-projected DOS) and limit_pref_atomic DOS (site-projected DOS) are set to 0, then the atomic DOS (site-projected DOS) will be ignored.

limit_pref_ados:
type: int | float, optional, default: 1.0
argument path: loss[dos]/limit_pref_ados

The prefactor of atomic DOS (site-projected DOS) loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_acdf:
type: int | float, optional, default: 0.0
argument path: loss[dos]/start_pref_acdf

The prefactor of Cumulative integral of atomic DOS loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the Cumulative integral of atomic DOS label should be provided by file Cumulative integral of atomic DOS.npy in each data system. If both start_pref_Cumulative integral of atomic DOS and limit_pref_Cumulative integral of atomic DOS are set to 0, then the Cumulative integral of atomic DOS will be ignored.

limit_pref_acdf:
type: int | float, optional, default: 0.0
argument path: loss[dos]/limit_pref_acdf

The prefactor of Cumulative integral of atomic DOS loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

When type is set to tensor:

(Supported Backend: TensorFlow)

pref:
type: int | float
argument path: loss[tensor]/pref

The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. ‘polarizability.npy` or dipole.npy, whose shape should be #frames x [9 or 3]. If it’s larger than 0.0, this npy should be included.

pref_atomic:
type: int | float
argument path: loss[tensor]/pref_atomic

The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. atomic_polarizability.npy or atomic_dipole.npy, whose shape should be #frames x ([9 or 3] x #selected atoms). If it’s larger than 0.0, this npy should be included. Both pref and pref_atomic should be provided, and either can be set to 0.0.

loss_dict:
type: dict, optional
argument path: loss_dict

The dictionary of definitions of multiple loss functions in multi-task mode. Each loss_dict[fitting_key], with user-defined name fitting_key in model/fitting_net_dict, is the single definition of loss function, whose type should be set to tensor, ener or left unset.

training:
type: dict
argument path: training

The training options.

training_data:
type: dict, optional
argument path: training/training_data

Configurations of training data.

systems:
type: str | typing.List[str]
argument path: training/training_data/systems

The data systems for training. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.

set_prefix:
type: str, optional, default: set
argument path: training/training_data/set_prefix

The prefix of the sets in the systems.

batch_size:
type: str | typing.List[int] | int, optional, default: auto
argument path: training/training_data/batch_size

This key can be

  • list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list.

  • int: all systems use the same batch size.

  • string “auto”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.

  • string “auto:N”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.

  • string “mixed:N”: the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor.

If MPI is used, the value should be considered as the batch size per task.

auto_prob:
type: str, optional, default: prob_sys_size, alias: auto_prob_style
argument path: training/training_data/auto_prob

Determine the probability of systems automatically. The method is assigned by this key and can be

  • “prob_uniform” : the probability all the systems are equal, namely 1.0/self.get_nsystems()

  • “prob_sys_size” : the probability of a system is proportional to the number of batches in the system

  • “prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;…” : the list of systems is devided into blocks. A block is specified by stt_idx:end_idx:weight, where stt_idx is the starting index of the system, end_idx is then ending (not including) index of the system, the probabilities of the systems in this block sums up to weight, and the relatively probabilities within this block is proportional to the number of batches in the system.

sys_probs:
type: typing.List[float] | NoneType, optional, default: None, alias: sys_weights
argument path: training/training_data/sys_probs

A list of float if specified. Should be of the same length as systems, specifying the probability of each system.

validation_data:
type: dict | NoneType, optional, default: None
argument path: training/validation_data

Configurations of validation data. Similar to that of training data, except that a numb_btch argument may be configured

systems:
type: str | typing.List[str]
argument path: training/validation_data/systems

The data systems for validation. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.

set_prefix:
type: str, optional, default: set
argument path: training/validation_data/set_prefix

The prefix of the sets in the systems.

batch_size:
type: str | typing.List[int] | int, optional, default: auto
argument path: training/validation_data/batch_size

This key can be

  • list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list.

  • int: all systems use the same batch size.

  • string “auto”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.

  • string “auto:N”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.

auto_prob:
type: str, optional, default: prob_sys_size, alias: auto_prob_style
argument path: training/validation_data/auto_prob

Determine the probability of systems automatically. The method is assigned by this key and can be

  • “prob_uniform” : the probability all the systems are equal, namely 1.0/self.get_nsystems()

  • “prob_sys_size” : the probability of a system is proportional to the number of batches in the system

  • “prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;…” : the list of systems is devided into blocks. A block is specified by stt_idx:end_idx:weight, where stt_idx is the starting index of the system, end_idx is then ending (not including) index of the system, the probabilities of the systems in this block sums up to weight, and the relatively probabilities within this block is proportional to the number of batches in the system.

sys_probs:
type: typing.List[float] | NoneType, optional, default: None, alias: sys_weights
argument path: training/validation_data/sys_probs

A list of float if specified. Should be of the same length as systems, specifying the probability of each system.

numb_btch:
type: int, optional, default: 1, alias: numb_batch
argument path: training/validation_data/numb_btch

An integer that specifies the number of batches to be sampled for each validation period.

mixed_precision:
type: dict, optional
argument path: training/mixed_precision

Configurations of mixed precision.

output_prec:
type: str, optional, default: float32
argument path: training/mixed_precision/output_prec

The precision for mixed precision params. “ “The trainable variables precision during the mixed precision training process, “ “supported options are float32 only currently.

compute_prec:
type: str
argument path: training/mixed_precision/compute_prec

The precision for mixed precision compute. “ “The compute precision during the mixed precision training process, “” “supported options are float16 and bfloat16 currently.

numb_steps:
type: int, alias: stop_batch
argument path: training/numb_steps

Number of training batch. Each training uses one batch of data.

seed:
type: int | NoneType, optional
argument path: training/seed

The random seed for getting frames from the training data set.

disp_file:
type: str, optional, default: lcurve.out
argument path: training/disp_file

The file for printing learning curve.

disp_freq:
type: int, optional, default: 1000
argument path: training/disp_freq

The frequency of printing learning curve.

save_freq:
type: int, optional, default: 1000
argument path: training/save_freq

The frequency of saving check point.

save_ckpt:
type: str, optional, default: model.ckpt
argument path: training/save_ckpt

The path prefix of saving check point files.

max_ckpt_keep:
type: int, optional, default: 5
argument path: training/max_ckpt_keep

The maximum number of checkpoints to keep. The oldest checkpoints will be deleted once the number of checkpoints exceeds max_ckpt_keep. Defaults to 5.

disp_training:
type: bool, optional, default: True
argument path: training/disp_training

Displaying verbose information during training.

time_training:
type: bool, optional, default: True
argument path: training/time_training

Timing durining training.

profiling:
type: bool, optional, default: False
argument path: training/profiling

(Supported Backend: TensorFlow) Profiling during training.

profiling_file:
type: str, optional, default: timeline.json
argument path: training/profiling_file

(Supported Backend: TensorFlow) Output file for profiling.

enable_profiler:
type: bool, optional, default: False
argument path: training/enable_profiler

Enable TensorFlow Profiler (available in TensorFlow 2.3) or PyTorch Profiler to analyze performance. The log will be saved to tensorboard_log_dir.

tensorboard:
type: bool, optional, default: False
argument path: training/tensorboard

Enable tensorboard

tensorboard_log_dir:
type: str, optional, default: log
argument path: training/tensorboard_log_dir

The log directory of tensorboard outputs

tensorboard_freq:
type: int, optional, default: 1
argument path: training/tensorboard_freq

The frequency of writing tensorboard events.

data_dict:
type: dict, optional
argument path: training/data_dict

The dictionary of multi DataSystems in multi-task mode. Each data_dict[fitting_key], with user-defined name fitting_key in model/fitting_net_dict, contains training data and optional validation data definitions.

fitting_weight:
type: dict, optional
argument path: training/fitting_weight

Each fitting_weight[fitting_key], with user-defined name fitting_key in model/fitting_net_dict, is the training weight of fitting net fitting_key. Fitting nets with higher weights will be selected with higher probabilities to be trained in one step. Weights will be normalized and minus ones will be ignored. If not set, each fitting net will be equally selected when training.

warmup_steps:
type: int, optional
argument path: training/warmup_steps

(Supported Backend: PyTorch) The number of steps for learning rate warmup. During warmup, the learning rate begins at zero and progressively increases linearly to start_lr, rather than starting directly from start_lr

gradient_max_norm:
type: float, optional
argument path: training/gradient_max_norm

(Supported Backend: PyTorch) Clips the gradient norm to a maximum value. If the gradient norm exceeds this value, it will be clipped to this limit. No gradient clipping will occur if set to 0.

stat_file:
type: str, optional
argument path: training/stat_file

(Supported Backend: PyTorch) The file path for saving the data statistics results. If set, the results will be saved and directly loaded during the next training session, avoiding the need to recalculate the statistics. If the file extension is .h5 or .hdf5, an HDF5 file is used to store the statistics; otherwise, a directory containing NumPy binary files are used.

Depending on the value of opt_type, different sub args are accepted.

opt_type:
type: str (flag key), default: Adam
argument path: training/opt_type
possible choices: Adam, LKF

(Supported Backend: PyTorch) The type of optimizer to use.

When opt_type is set to Adam:

When opt_type is set to LKF:

kf_blocksize:
type: int, optional
argument path: training[LKF]/kf_blocksize

(Supported Backend: PyTorch) The blocksize for the Kalman filter.

nvnmd:
type: dict, optional
argument path: nvnmd

The nvnmd options.

version:
type: int
argument path: nvnmd/version

configuration the nvnmd version (0 | 1), 0 for 4 types, 1 for 32 types

max_nnei:
type: int
argument path: nvnmd/max_nnei

configuration the max number of neighbors, 128|256 for version 0, 128 for version 1

net_size:
type: int
argument path: nvnmd/net_size

configuration the number of nodes of fitting_net, just can be set as 128

map_file:
type: str
argument path: nvnmd/map_file

A file containing the mapping tables to replace the calculation of embedding nets

config_file:
type: str
argument path: nvnmd/config_file

A file containing the parameters about how to implement the model in certain hardware

weight_file:
type: str
argument path: nvnmd/weight_file

a *.npy file containing the weights of the model

enable:
type: bool
argument path: nvnmd/enable

enable the nvnmd training

restore_descriptor:
type: bool
argument path: nvnmd/restore_descriptor

enable to restore the parameter of embedding_net from weight.npy

restore_fitting_net:
type: bool
argument path: nvnmd/restore_fitting_net

enable to restore the parameter of fitting_net from weight.npy

quantize_descriptor:
type: bool
argument path: nvnmd/quantize_descriptor

enable the quantizatioin of descriptor

quantize_fitting_net:
type: bool
argument path: nvnmd/quantize_fitting_net

enable the quantizatioin of fitting_net