docs: update docs for type_one_side

deepmd/utils/argcheck.py

@@ -132,7 +132,7 @@ def descrpt_se_a_args():
     doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).'
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
+    doc_type_one_side = r'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.'
     doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
     doc_trainable = 'If the parameters in the embedding net is trainable'
     doc_seed = 'Random seed for parameter initialization'
@@ -209,7 +209,7 @@ def descrpt_se_r_args():
     doc_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.'
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
+    doc_type_one_side = r'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.'
     doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
     doc_trainable = 'If the parameters in the embedding net are trainable'
     doc_seed = 'Random seed for parameter initialization'
@@ -253,7 +253,7 @@ def descrpt_se_atten_args():
     doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).'
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = 'Whether to consider the information from only one side or both sides.'
+    doc_type_one_side = r'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.'
     doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
     doc_trainable = 'If the parameters in the embedding net is trainable'
     doc_seed = 'Random seed for parameter initialization'

doc/model/train-se-e2-a.md

@@ -28,7 +28,7 @@ The construction of the descriptor is given by section {ref}`descriptor <model/d
 * {ref}`rcut <model/descriptor[se_e2_a]/rcut>` is the cut-off radius for neighbor searching, and the {ref}`rcut_smth <model/descriptor[se_e2_a]/rcut_smth>` gives where the smoothing starts. 
 * {ref}`sel <model/descriptor[se_e2_a]/sel>` gives the maximum possible number of neighbors in the cut-off radius. It is a list, the length of which is the same as the number of atom types in the system, and `sel[i]` denotes the maximum possible number of neighbors with type `i`. 
 * The {ref}`neuron <model/descriptor[se_e2_a]/neuron>` specifies the size of the embedding net. From left to right the members denote the sizes of each hidden layer from the input end to the output end, respectively. If the outer layer is twice the size of the inner layer, then the inner layer is copied and concatenated, then a [ResNet architecture](https://arxiv.org/abs/1512.03385) is built between them.
-* If the option {ref}`type_one_side <model/descriptor[se_e2_a]/type_one_side>` is set to `true`, then the descriptor will consider the types of neighbor atoms. Otherwise, both the types of centric and neighbor atoms are considered.
+* If the option {ref}`type_one_side <model/descriptor[se_e2_a]/type_one_side>` is set to `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.
 * The {ref}`axis_neuron <model/descriptor[se_e2_a]/axis_neuron>` specifies the size of the submatrix of the embedding matrix, the axis matrix as explained in the [DeepPot-SE paper](https://arxiv.org/abs/1805.09003) 
 * If the option {ref}`resnet_dt <model/descriptor[se_e2_a]/resnet_dt>` is set to `true`, then a timestep is used in the ResNet.
 * {ref}`seed <model/descriptor[se_e2_a]/seed>` gives the random seed that is used to generate random numbers when initializing the model parameters.