train-se-a-mask.md

Descriptor "se_a_mask"

Descriptor se_a_mask is a concise implementation of the descriptor se_e2_a, but functions slightly differently. se_a_mask is specially designed for DP/MM simulations where the number of atoms in DP regions is dynamically changed in simulations.

Therefore, the descriptor se_a_mask is not supported for training with PBC systems for simplicity. Besides, to make the output shape of the descriptor matrix consistent, the input coordinates are padded with virtual particle coordinates to the maximum number of atoms (specified with sel in the descriptor setting) in the system. The real/virtual sign of the atoms is specified with the aparam.npy ( [ nframes * natoms ] ) file in the input systems set directory. The aparam.npy can also be seen as the mask of the atoms in the system, which is also the origin of the name se_a_mask.

In this example, we will train a DP Mask model for zinc protein interactions. The input systems are the collection of zinc and its coordinates residues. A sample input system that contains 2 frames is included in the directory.

$deepmd_source_dir/examples/zinc_protein/data_dp_mask

A complete training input script of this example can be found in the directory.

$deepmd_source_dir/examples/zinc_protein/zinc_se_a_mask.json

The construction of the descriptor is given by section {ref}descriptor <model/descriptor>. An example of the descriptor is provided as follows

	"descriptor" :{
	    "type":	"se_a_mask",
	    "sel":		[36, 16, 24, 64, 6, 1],
	    "neuron":		[25, 50, 100],
		"axis_neuron": 16,
	    "type_one_side":	false,
	    "resnet_dt":	false,
	    "seed":		1
	}

• The {ref}type <model/descriptor/type> of the descriptor is set to "se_a_mask".
• {ref}sel <model/descriptor[se_a_mask]/sel> gives the maximum number of atoms in input coordinates. 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 number of atoms with type i.
• The {ref}neuron <model/descriptor[se_a_mask]/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 is built between them.
• The {ref}axis_neuron <model/descriptor[se_a_mask]/axis_neuron> specifies the size of the submatrix of the embedding matrix, the axis matrix as explained in the DeepPot-SE paper
• If the option {ref}type_one_side <model/descriptor[se_a_mask]/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.
• If the option {ref}resnet_dt <model/descriptor[se_a_mask]/resnet_dt> is set to true, then a timestep is used in the ResNet.
• {ref}seed <model/descriptor[se_a_mask]/seed> gives the random seed that is used to generate random numbers when initializing the model parameters.

To make the aparam.npy used for descriptor se_a_mask, two variables in fitting_net section are needed.

	"fitting_net" :{
	    "neuron": [240, 240, 240],
      	"resnet_dt": true,
      	"seed": 1,
      	"numb_aparam": 1,
      	"use_aparam_as_mask": true
	}

• neuron, resnet_dt and seed are the same as the {ref}fitting_net <model/fitting_net[ener]> section for fitting energy.
• {ref}numb_aparam <model/fitting_net[ener]/numb_aparam> gives the dimesion of the aparam.npy file. In this example, it is set to 1 and stores the real/virtual sign of the atoms. For real/virtual atoms, the corresponding sign in aparam.npy is set to 1/0.
• {ref}use_aparam_as_mask <model/fitting_net[ener]/use_aparam_as_mask> is set to true to use the aparam.npy as the mask of the atoms in the descriptor se_a_mask.

Finally, to make a reasonable fitting task with se_a_mask descriptor for DP/MM simulations, the loss function with se_a_mask is designed to include the atomic forces difference in specific atoms of the input particles only. More details about the selection of the specific atoms can be found in paper [DP/MM](left to be filled). Thus, atom_pref.npy ( [ nframes * natoms ] ) is required as the indicator of the specific atoms in the input particles. And the loss section in the training input script should be set as follows.

"loss": {
    "type": "ener",
    "start_pref_e": 0.0,
    "limit_pref_e": 0.0,
    "start_pref_f": 0.0,
    "limit_pref_f": 0.0,
    "start_pref_pf": 1.0,
    "limit_pref_pf": 1.0,
    "_comment": " that's all"
  }