add an example of nopbc and related docs

doc/data/data-conv.md

@@ -50,3 +50,6 @@ $ ls
 box.raw  coord.raw  energy.raw  force.raw  set.000  set.001  set.002  type.raw  virial.raw
 ```
 It generates three sets `set.000`, `set.001` and `set.002`, with each set contains 2000 frames. One do not need to take care of the binary data files in each of the `set.*` directories. The path containing `set.*` and `type.raw` is called a *system*. 
+
+If one needs to train a non-periodic system, an empty `nopbc` file should be put under the system directory. `box.raw` is not necessary is a non-periodic system.
+

doc/train/training-advanced.md

@@ -45,7 +45,7 @@ Other training parameters are given in the `training` section.
     }
 ```
 The sections `"training_data"` and `"validation_data"` give the training dataset and validation dataset, respectively. Taking the training dataset for example, the keys are explained below:
-* `systems` provide paths of the training data systems. DeePMD-kit allows you to provide multiple systems. This key can be a `list` or a `str`.
+* `systems` provide paths of the training data systems. DeePMD-kit allows you to provide multiple systems with different numbers of atoms. This key can be a `list` or a `str`.
     * `list`: `systems` gives the training data systems.
     * `str`: `systems` should be a valid path. DeePMD-kit will recursively search all data systems in this path.
 * At each training step, DeePMD-kit randomly pick `batch_size` frame(s) from one of the systems. The probability of using a system is by default in proportion to the number of batches in the system. More optional are available for automatically determining the probability of using systems. One can set the key `auto_prob` to
@@ -118,4 +118,4 @@ One can set other environmental variables:
 
 | Environment variables | Allowed value          | Default value | Usage                      |
 | --------------------- | ---------------------- | ------------- | -------------------------- |
-| DP_INTERFACE_PREC     | `high`, `low`          | `high`        | Control high (double) or low (float) precision of training. |
+| DP_INTERFACE_PREC     | `high`, `low`          | `high`        | Control high (double) or low (float) precision of training. |

examples/nopbc/README.md

@@ -0,0 +1,8 @@
+## Example of training non-periodic data
+
+This is an example of training Deep Potential models using non-periodic data with flexible sizes. The example is extracted from the following reference.
+
+It's warned that the example data is of very limited amount, so it cannot be put into production. Full data set can be downloaded from the following reference.
+
+Zeng, J., Cao, L., Xu, M. et al. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat Commun 11, 5713 (2020). DOI: [10.1038/s41467-020-19497-z](https://doi.org/10.1038/s41467-020-19497-z)
+

examples/nopbc/data/C1H4O2/type.raw

@@ -0,0 +1,7 @@
+1
+1
+1
+1
+0
+2
+2

examples/nopbc/data/C1H4O2/type_map.raw

@@ -0,0 +1,3 @@
+C
+H
+O

examples/nopbc/data/C3H3O4/type.raw

@@ -0,0 +1,10 @@
+1
+1
+1
+0
+0
+0
+2
+2
+2
+2

examples/nopbc/data/C3H3O4/type_map.raw

@@ -0,0 +1,3 @@
+C
+H
+O

examples/nopbc/data/C4H3O1/type.raw

@@ -0,0 +1,8 @@
+1
+1
+1
+0
+0
+0
+0
+2

examples/nopbc/data/C4H3O1/type_map.raw

@@ -0,0 +1,3 @@
+C
+H
+O

examples/nopbc/train/input.json

@@ -0,0 +1,60 @@
+{
+    "_comment": " model parameters",
+    "model": {
+	"type_map":	["C", "H", "O"],
+	"descriptor" :{
+	    "type":		"se_e2_a",
+	    "sel":		[40, 80, 40],
+	    "rcut_smth":	1.00,
+	    "rcut":		6.00,
+	    "neuron":		[25, 50, 100],
+	    "resnet_dt":	false,
+	    "axis_neuron":	12,
+	    "seed":		1,
+	    "_comment":		" that's all"
+	},
+	"fitting_net" : {
+	    "neuron":		[240, 240, 240],
+	    "resnet_dt":	true,
+	    "seed":		1,
+	    "_comment":		" that's all"
+	},
+	"_comment":	" that's all"
+    },
+
+    "learning_rate" :{
+	"type":		"exp",
+	"decay_steps":	4000,
+	"start_lr":	0.001,	
+	"stop_lr":	3.51e-8,
+	"_comment":	"that's all"
+    },
+
+    "loss" :{
+	"type":		"ener",
+	"start_pref_e":	0.02,
+	"limit_pref_e":	1,
+	"start_pref_f":	1000,
+	"limit_pref_f":	1,
+	"start_pref_v":	0,
+	"limit_pref_v":	0,
+	"_comment":	" that's all"
+    },
+
+    "training" : {
+	"training_data": {
+	    "systems":		"../data/",
+	    "batch_size":	"auto",
+	    "_comment":		"that's all"
+	},
+	"numb_steps":	4000000,
+	"seed":		10,
+	"disp_file":	"lcurve.out",
+	"disp_freq":	100,
+	"save_freq":	1000,
+	"_comment":	"that's all"
+    },    
+
+    "_comment":		"that's all"
+}
+