Potential not stable for LiAlO2

[ankit213910]

I am a new user of DeepMD.
I have trained a Deepmd model for LiAlO2 system.
I used the AIMD VASP OUTCAR to prepare the trianing set. I used the following code to prepare the training set.

from dpdata import LabeledSystem, MultiSystems
from glob import glob
"""
process multi systems
"""
fs = glob("OUTCAR") # remeber to change here !!!
ms = MultiSystems()
for f in fs:
try:
ls = LabeledSystem(f)
except:
print(f)
if len(ls) > 0:
ms.append(ls)
ms.to_deepmd_raw("deepmd")
ms.to_deepmd_npy("deepmd")

MultiSystems (1 systems containing 6000 frames)

It created the following files which includes the force and stress.

box, coord, energy, force, type, type_map, virial
(image shown below)

Then I trained it using the input_compress2_json (modified from the model compression json) and got the model.ckpt. I froze the model and used it in LAMMPS. But the potential is not able to retain simple crystal structure of a 500 atom supercell. Input.jaosn shown below

{
"_comment1": " model parameters",
"model": {
"type_map": [
"H",
"Li",
"Al",
"O"
],
"descriptor": {
"type": "se_e2_a",
"sel": [
46,
92,
138,
184
],
"rcut_smth": 0.50,
"rcut": 6.00,
"_comment": "N2=2N1, N2=N1, and otherwise can be tested",
"neuron": [
4,
8,
17,
17
],
"resnet_dt": false,
"axis_neuron": 16,
"seed": 1,
"_comment2": " that's all"
},
"fitting_net": {
"neuron": [
20,
20,
20
],
"resnet_dt": true,
"seed": 1,
"_comment3": " that's all"
},
"_comment4": " that's all"
},

"learning_rate": {
"type": "exp",
"decay_steps": 5000,
"start_lr": 0.001,
"stop_lr": 3.51e-8,
"_comment5": "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,
"_comment6": " that's all"
},

"training": {
"training_data": {
"systems": [
"model_compression2/data"
],
"batch_size": "auto",
"_comment7": "that's all"
},
"validation_data": {
"systems": [
"model_compression2/data"
],
"batch_size": 1,
"numb_btch": 3,
"_comment8": "that's all"
},
"numb_steps": 1000,
"seed": 10,
"disp_file": "lcurve.out",
"disp_freq": 100,
"save_freq": 1000,
"_comment9": "that's all"
},

"_comment10": "that's all"
}

I wanted to take some suggestions from you regarding how to get a simple stable potential using DeepMD. Could you kindly take a look at the data and the input file I sent please? I appreciate your guidance.

[wanghan-iapcm]

I find the number of neurons is very limited have you tried with the default settings in the deepmd-kit water example?

and

How about the training and test accuracy of your model?