Skip to content

Commit

Permalink
fix grammatical errors (#2796)
Browse files Browse the repository at this point in the history
Fix grammatical errors in the document.
  • Loading branch information
Yi-FanLi authored Sep 15, 2023
1 parent 20a41d0 commit 5591ed1
Showing 1 changed file with 1 addition and 1 deletion.
2 changes: 1 addition & 1 deletion doc/development/type-embedding.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# 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 $\mathcal R$. It is consist of the distance between the centric atom and its neighbors, as well as a direction vector. We can embed each distance into a vector of $M_1$ dimension by an `embedding net`, so the environment matrix $\mathcal R$ can be embedded into matrix $\mathcal G$. We can thus extract a descriptor vector (of $M_1 \times M_2$ dim) of the centric atom from the $\mathcal G$ by some matrix multiplication, and put the descriptor into `fitting net` to get predicted energy $E$. The vanilla version of DeePMD-kit builds `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.
Here is an overview of the DeePMD-kit algorithm. Given a specific centric atom, we can obtain the matrix describing its local environment, named $\mathcal R$. It consists of the distance between the centric atom and its neighbors, as well as a direction vector. We can embed each distance into a vector of $M_1$ dimension by an `embedding net`, so the environment matrix $\mathcal R$ can be embedded into matrix $\mathcal G$. We can thus extract a descriptor vector (of $M_1 \times M_2$ dim) of the centric atom from the $\mathcal G$ by some matrix multiplication, and put the descriptor into `fitting net` to get the predicted energy $E$. The vanilla version of DeePMD-kit builds `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 reduces training complexity largely.

## Preliminary
In the following chart, you can find the meaning of symbols used to clarify the atom-type embedding algorithm.
Expand Down

0 comments on commit 5591ed1

Please sign in to comment.