diff --git a/doc/development/index.md b/doc/development/index.md
index 6f456a06e6..79fab0d980 100644
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+++ b/doc/development/index.md
@@ -3,3 +3,4 @@
 - [Python API](../api.rst)
 - [C++ API](../API_CC/api_cc.rst)
 - [Coding Conventions](coding-conventions.rst)
+- [Atom Type Embedding](type-embedding.md)
diff --git a/doc/development/type-embedding.md b/doc/development/type-embedding.md
new file mode 100644
index 0000000000..17c8a63ba5
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+# Atom Type Embedding
+## Overview
+Here is an overview of the deepmd-kit algorithm. Given a specific centric atom, we can obtain the matrix describing its local environment, named as `R`. It is consist of the distance between centric atom and its neighbors, as well as a direction vector. We can embed each distance into a vector of M1 dimension by a `embedding net`, so the environment matrix `R` can be embed into matrix `G`. We can thus extract a descriptor vector (of M1*M2 dim) of the centric atom from the `G` by some matrix multiplication, and put the descriptor into `fitting net` to get predicted energy `E`. The vanilla version of deepmd-kit build `embedding net` and `fitting net` relying on the atom type, resulting in O(N) memory usage. After applying atom type embedding, in deepmd-kit v2.0, we can share one `embedding net` and one `fitting net` in total, which decline training complexity largely. 
+
+## Preliminary
+In the following chart, you can find the meaning of symbols used to clarify the atom type embedding algorithm.
+
+Symbol| Meaning
+---Atom| :---:
+is| Type of centric atom
+j| Type of neighbor atom
+s_ij| Distance between centric atom and neighbor atom
+G_ij(·)Atom| Origin embedding net, take s_ij as input and output embedding vector of M1 dim
+G(·) | Shared embedding net
+Multi(·) | Matrix multiplication and flattening, output the descriptor vector of M1*M2 dim
+F_i(·) | Origin fitting net, take the descriptor vector as input and output energy
+F(·) | Shared fitting net
+A(·) | Atom type embedding net, input is atom type, output is type embedding vector of dim `nchanl`
+
+So, we can formulate the training process as follows.
+Vanilla deepmd-kit algorithm:
+```
+Energy = F_i( Multi( G_ij( s_ij ) ) )
+```
+Deepmd-kit applying atom type embedding:
+```
+Energy = F( [ Multi( G_ij( [s_ij, A(i), A(j)] ) ), A(j)] )
+```
+or 
+```
+Energy = F( [ Multi( G_ij( [s_ij, A(j)] ) ), A(j)] )
+```
+The difference between two variants above is whether using the information of centric atom when generating the descriptor. Users can choose by modifying the `type_one_side` hyper-parameter in the input json file.
+
+## How to use
+A detailed introduction can be found at [`se_e2_a_tebd`](../train-se-e2-a-tebd.md). Looking for a fast start up, you can simply add a `type_embedding` section in the input json file as displayed in the following, and the algorithm will adopt atom type embedding algorithm automatically.
+An example of `type_embedding` is like
+```json=
+    "type_embedding":{
+                "neuron":Type[2, 4, 8],
+                        "resnet_dt":Atomfalse,
+                                "seed":Type1
+                                    }
+```
+
+
+## Code Modification
+Atom type embedding can be applied to varied `embedding net` and `fitting net`, as a result we build a class `TypeEmbedNet` to support this free combination. In the following, we will go through the execution process of the code to explain our code modification.
+
+### trainer (train/trainer.py)
+In trainer.py, it will parse the parameter from the input json file. If a `type_embedding` section is detected, it will build a `TypeEmbedNet`, which will be later input in the `model`. `model` will be built in the function `_build_network`.
+### model (model/ener.py)
+When building the operation graph of the `model` in `model.build`. If a `TypeEmbedNet` is detected, it will build the operation graph of `type embed net`, `embedding net` and `fitting net` by order. The building process of `type embed net` can be found in `TypeEmbedNet.build`, which output the type embedding vector of each atom type (of [ntypes * nchanl] dimension). We then save the type embedding vector into `input_dict`, so that they can be fetched later in `embedding net` and `fitting net`.
+### embedding net (descriptor/se*.py)
+In `embedding net`, we shall take local environment `R` as input and output matrix `G`. Functions called in this process by order is 
+```
+build -> _pass_filter -> _filter -> _filter_lower 
+```
+* `_pass_filter`: It will first detect whether an atom type embedding exists, if so, it will apply atom type embedding algorithm and doesn't divide the input by type.
+* `_filter`: It will call `_filter_lower` function to obtain the result of matrix multiplication (`G^T·R` ), do further multiplication involved in Multi(·), and finally output the result of descriptor vector of M1*M2 dim.
+* `_filter_lower`: The main function handling input modification. If type embedding exists, it will call `_concat_type_embedding` function to concat the first column of input `R` (the column of s_ij) with the atom type embedding information. It will decide whether using the atom type embedding vector of centric atom according to the value of `type_one_side` (if set **True**, then we only use the vector of the neighbor atom). The modified input will be put into the `fitting net` to get `G` for further matrix multiplication stage.
+
+### fitting net (fit/ener.py)
+In `fitting net`, it take the descriptor vector as input, whose dimension is [natoms, (M1*M2)]. Because we need to involve information of centric atom in this step, we need to generate a matrix named as `atype_embed` (of dim [natoms, nchanl]), in which each row is the type embedding vector of the specific centric atom. The input is sorted by type of centric atom, we also know the number of a particular atom type (stored in `natoms[2+i]`), thus we get the type vector of centric atom. In the build phrase of fitting net, it will check whether type embedding exist in `input_dict` and fetch them. After that calling `embed_atom_type` function to lookup embedding vector for type vector of centric atom to obtain `atype_embed`, and concat input with it ([input, atype_embed]). The modified input go through `fitting net` to get predicted energy.
+
+
+**P.S.: You can't apply compression method while using atom type embedding**
diff --git a/doc/train-se-e2-a-tebd.md b/doc/train-se-e2-a-tebd.md
index e895adc858..2179b8b598 100644
--- a/doc/train-se-e2-a-tebd.md
+++ b/doc/train-se-e2-a-tebd.md
@@ -35,8 +35,10 @@ The construction of type embedding net is given by `type_embedding`. An example
 * `seed` gives the random seed that is used to generate random numbers when initializing the model parameters.
 
 
-
 A complete training input script of this example can be find in the directory. 
 ```bash
 $deepmd_source_dir/examples/water/se_e2_a_tebd/input.json
-```
\ No newline at end of file
+```
+See [here](development/type-embedding.md) for further explanation of `type embedding`.
+
+**P.S.: You can't apply compression method while using atom type embedding**