From ee0f6e6aa789e06a297d5e6f76000356b808474e Mon Sep 17 00:00:00 2001
From: jxxiaoshaoye <53417696+jxxiaoshaoye@users.noreply.github.com>
Date: Fri, 13 Aug 2021 11:14:59 +0800
Subject: [PATCH] update type embedding doc (#967)

---
 doc/development/type-embedding.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/doc/development/type-embedding.md b/doc/development/type-embedding.md
index 17c8a63ba5..c1fb72979d 100644
--- a/doc/development/type-embedding.md
+++ b/doc/development/type-embedding.md
@@ -5,17 +5,17 @@ Here is an overview of the deepmd-kit algorithm. Given a specific centric atom,
 ## 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`
+|Symbol| Meaning|
+|---| :---:|
+|i| Type of centric atom|
+|j| Type of neighbor atom|
+|s_ij| Distance between centric atom and neighbor atom|
+|G_ij(·)| 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:
@@ -24,11 +24,11 @@ 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)] )
+Energy = F( [ Multi( G( [s_ij, A(i), A(j)] ) ), A(j)] )
 ```
 or 
 ```
-Energy = F( [ Multi( G_ij( [s_ij, A(j)] ) ), A(j)] )
+Energy = F( [ Multi( G( [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.