docs: DPRc for PT, DPModel (#3373)

It should be merged after #3370. Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
deepmodeling · Mar 1, 2024 · 2594f09 · 2594f09
1 parent f684be8
commit 2594f09
Showing 1 changed file with 49 additions and 2 deletions.
diff --git a/doc/model/dprc.md b/doc/model/dprc.md
@@ -1,7 +1,7 @@
-# Deep Potential - Range Correction (DPRc) {{ tensorflow_icon }}
+# Deep Potential - Range Correction (DPRc) {{ tensorflow_icon }} {{ pytorch_icon }} {{ dpmodel_icon }}
 
 :::{note}
-**Supported backends**: TensorFlow {{ tensorflow_icon }}
+**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, DPModel {{ dpmodel_icon }}
 :::
 
 Deep Potential - Range Correction (DPRc) is designed to combine with QM/MM method, and corrects energies from a low-level QM/MM method to a high-level QM/MM method:
@@ -62,6 +62,10 @@ In a DPRc model, QM atoms and MM atoms have different atom types. Assuming we ha
 
 As described in the paper, the DPRc model only corrects $E_\text{QM}$ and $E_\text{QM/MM}$ within the cutoff, so we use a hybrid descriptor to describe them separatedly:
 
+::::{tab-set}
+
+:::{tab-item} TensorFlow {{ tensorflow_icon }}
+
 ```json
 "descriptor" :{
     "type":             "hybrid",
@@ -91,6 +95,45 @@ As described in the paper, the DPRc model only corrects $E_\text{QM}$ and $E_\te
 }
 ```
 
+:::
+
+:::{tab-item} PyTorch {{ pytorch_icon }}
+
+```json
+"descriptor" :{
+    "type":             "hybrid",
+    "list" : [
+        {
+            "type":     "se_e2_a",
+            "sel":              [6, 11, 0, 6, 0, 1],
+            "rcut_smth":        1.00,
+            "rcut":             9.00,
+            "neuron":           [12, 25, 50],
+            "exclude_types":    [[2, 2], [2, 4], [4, 4], [0, 2], [0, 4], [1, 2], [1, 4], [3, 2], [3, 4], [5, 2], [5, 4]],
+            "axis_neuron":      12,
+            "type_one_side":    true,
+            "_comment": " QM/QM interaction"
+        },
+        {
+            "type":     "se_e2_a",
+            "sel":              [6, 11, 100, 6, 50, 1],
+            "rcut_smth":        0.50,
+            "rcut":             6.00,
+            "neuron":           [12, 25, 50],
+            "exclude_types":    [[0, 0], [0, 1], [0, 3], [0, 5], [1, 1], [1, 3], [1, 5], [3, 3], [3, 5], [5, 5], [2, 2], [2, 4], [4, 4]],
+            "axis_neuron":      12,
+            "set_davg_zero":    true,
+            "type_one_side":    true,
+            "_comment": " QM/MM interaction"
+        }
+    ]
+}
+```
+
+:::
+
+::::
+
 {ref}`exclude_types <model/descriptor[se_a_ebd_v2]/exclude_types>` can be generated by the following Python script:
 ```py
 from itertools import combinations_with_replacement, product
@@ -131,6 +174,10 @@ The DPRc model has the best practices with the [AMBER](../third-party/out-of-dee
 
 ## Pairwise DPRc
 
+:::{note}
+**Supported backends**: TensorFlow {{ tensorflow_icon }}
+:::
+
 If one wants to correct from a low-level method into a full DFT level, and the system is too large to do full DFT calculation, one may try the experimental pairwise DPRc model.
 In a pairwise DPRc model, the total energy is divided into QM internal energy and the sum of QM/MM energy for each MM residue $l$: