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deepmodeling / deepmd-kit

A deep learning package for many-body potential energy representation and molecular dynamics
https://docs.deepmodeling.com/projects/deepmd/
GNU Lesser General Public License v3.0
1.46k stars 504 forks source link

[BUG] use_srtab lead to temperature explosion in the latest version #4054

Open tuoping opened 1 month ago

tuoping commented 1 month ago

Bug summary

When I run MD using the model trained with "use_srtab", the temperature explodes.

DeePMD-kit Version

v2.2.8

Backend and its version

TensorFlow v2.9.0.

How did you download the software?

conda

Input Files, Running Commands, Error Log, etc.

input.json

{
    "model": {
          "use_srtab": "zbl-all.txt",
          "smin_alpha": 0.1,
          "sw_rmin": 2.000000000000000042e-02,
          "sw_rmax": 2.479999999999999982e+00,
          "type_map": [
              "I",
              "Pb",
              "C",
              "N",
              "H"
          ],
          "descriptor": {
              "type": "se_atten",
              "stripped_type_embedding": true,
              "sel": 100,
              "rcut_smth": 0.50,
              "rcut": 6.00,
              "neuron": [
                25,
                50,
                100
              ],
              "resnet_dt": false,
              "axis_neuron": 16,
              "seed": 18225,
              "attn": 0,
              "attn_layer": 0,
              "attn_dotr": true,
              "attn_mask": false,
              "precision": "float64",
              "_comment2": " that's all"
            },

          "fitting_net": {
              "neuron": [
                  240,
                  240,
                  240
              ],
              "resnet_dt": true,
              "seed": 12741,
              "activation_function": "tanh"
          }
      },
    "learning_rate": {
        "type": "exp",
        "start_lr": 0.001,
        "decay_steps": 50000
    },
    "loss": {
        "start_pref_e": 0.02,
        "limit_pref_e": 2,
        "start_pref_f": 1000,
        "limit_pref_f": 2,
        "start_pref_v": 0.01,
        "limit_pref_v": 1
    },
    "training": {
        "stop_batch": 10000000,
        "batch_size": "auto",
        "disp_file": "lcurve.out",
        "disp_freq": 50000,
        "numb_test": 4,
        "save_freq": 50000,
        "save_ckpt": "model.ckpt",
        "disp_training": true,
        "time_training": true,
        "profiling": false,
        "profiling_file": "timeline.json",
        "_comment": "that's all",
        "systems": [
            "coldata_03/"
        ],
        "seed": 14711
    }
}

use_srtab file "zbl-all.txt"

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input.lammps file for lammps MD


units           metal
boundary        p p p
atom_style      atomic

neighbor        1.0 bin

box tilt large
read_data conf.lmp
read_dump prev.dump 0 x y z vx vy vz
mass            1 127.000000
mass            2 207.000000
mass            3 12.000000
mass            4 14.000000
mass            5 10.000000

pair_style      deepmd model.pb
pair_coeff      * *

thermo_style    custom step temp pe ke etotal press pxx pyy pzz pxy pyz pxz vol lx ly lz
thermo          1
dump            1 all custom 1 traj.dump id type x y z vx vy vz fx fy fz
restart         10000 dpgen.restart

timestep        0.001

fix             1 all nve
fix             2 all langevin 300 300 0.1 658845

run             2000

unfix 1 
unfix 2
fix             1 all nph tri 0.0 0.0 1.0
fix             2 all langevin 300 300 0.1 658845

run             2000

Steps to Reproduce

dp train input.json
dp freeze
mkdir testmd/
cp conf.lmp prev.dump input.lammps testmd/
cp frozen_model.pb testmd/model.pb
lmp_mpi -i input.lammps

Further Information, Files, and Links

No response

njzjz commented 1 month ago

When performing the NVE simulation, does the total energy keep the constant?

tuoping commented 1 month ago

No. Total energy explodes very fast.

LAMMPS (23 Jun 2022 - Update 2)

units           metal
boundary        p p p
atom_style      atomic

neighbor        1.0 bin

box tilt large
read_data conf.lmp
Reading data file ...
  triclinic box = (0 0 0) to (41.085245 41.271957 35.901696) with tilt (-5.092044 20.286428 -0.106271)
  1 by 1 by 1 MPI processor grid
  reading atoms ...
  2592 atoms
  read_data CPU = 0.005 seconds
read_dump prev.dump 0 x y z vx vy vz
Scanning dump file ...
Reading snapshot from dump file ...
  triclinic box = (0 0 0) to (41.085245 41.271957 35.901696) with tilt (-5.092044 20.286428 -0.106271)
  2592 atoms before read
  2592 atoms in snapshot
  0 atoms purged
  2592 atoms replaced
  0 atoms trimmed
  0 atoms added
  2592 atoms after read
mass            1 127.000000
mass            2 207.000000
mass            3 12.000000
mass            4 14.000000
mass            5 10.000000

pair_style      deepmd model.pb
Summary of lammps deepmd module ...
  >>> Info of deepmd-kit:
  installed to:       /root/deepmd-kit/dp_c
  source:             
  source branch:      
  source commit:      
  source commit at:   
  support model ver.: 1.1 
  build variant:      cuda
  build with tf inc:  /usr/local/lib/python3.11/dist-packages/tensorflow/include;/usr/local/lib/python3.11/dist-packages/tensorflow/include
  build with tf lib:  /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_cc.so.2
  set tf intra_op_parallelism_threads: 0
  set tf inter_op_parallelism_threads: 0
  >>> Info of lammps module:
  use deepmd-kit at:  /nfs/scistore14/chenggrp/ptuo/pkgs/deepmd-2.2.8
  source:             
  source branch:      
  source commit:      
  source commit at:   
  build float prec:   double
  build with tf inc:  /nfs/scistore14/chenggrp/ptuo/pkgs/deepmd-2.2.8/include;/nfs/scistore14/chenggrp/ptuo/pkgs/deepmd-2.2.8/include
  build with tf lib:  /nfs/scistore14/chenggrp/ptuo/pkgs/deepmd-2.2.8/lib/libtensorflow_cc.so;/nfs/scistore14/chenggrp/ptuo/pkgs/deepmd-2.2.8/lib/libtensorflow_framework.so
pair_coeff      * *

thermo_style    custom step temp pe ke etotal press pxx pyy pzz pxy pyz pxz vol lx ly lz
thermo          1
dump            1 all custom 1 traj.dump id type x y z vx vy vz fx fy fz
restart         10000 dpgen.restart

timestep        0.001

fix             1 all nve
fix             2 all langevin 300 300 0.1 658845

run             2000

CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE

Your simulation uses code contributions which should be cited:

- USER-DEEPMD package:

@article{Wang_ComputPhysCommun_2018_v228_p178,
  author = {Wang, Han and Zhang, Linfeng and Han, Jiequn and E, Weinan},
  doi = {10.1016/j.cpc.2018.03.016},
  url = {https://doi.org/10.1016/j.cpc.2018.03.016},
  year = 2018,
  month = {jul},
  publisher = {Elsevier {BV}},
  volume = 228,
  journal = {Comput. Phys. Commun.},
  title = {{DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics}},
  pages = {178--184}
}

CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE

Generated 0 of 10 mixed pair_coeff terms from geometric mixing rule
Neighbor list info ...
  update every 1 steps, delay 10 steps, check yes
  max neighbors/atom: 2000, page size: 100000
  master list distance cutoff = 7
  ghost atom cutoff = 7
  binsize = 3.5, bins = 19 12 11
  1 neighbor lists, perpetual/occasional/extra = 1 0 0
  (1) pair deepmd, perpetual
      attributes: full, newton on
      pair build: full/bin/atomonly
      stencil: full/bin/3d
      bin: standard
Per MPI rank memory allocation (min/avg/max) = 5.109 | 5.109 | 5.109 Mbytes
   Step          Temp          PotEng         KinEng         TotEng         Press           Pxx            Pyy            Pzz            Pxy            Pyz            Pxz           Volume           Lx             Ly             Lz      
         0   300           -11806.534      100.47391     -11706.06       32752.211      43211.956      23157.158      31887.519     -2959.2177     -6627.8603     -12045.807      60877.374      41.085245      41.271957      35.901696    
         1   408.23218     -11914.452      136.72228     -11777.73       28105.831      38728.651      19668.961      25919.88      -3504.4857     -4713.8018     -10428.11       60877.374      41.085245      41.271957      35.901696    
         2   838.74206     -12066.568      280.90565     -11785.662      14792.731      24038.096      11321.927      9018.1707     -6821.6638     -4943.334      -9347.6846      60877.374      41.085245      41.271957      35.901696    
         3   1233.3138     -12101.783      413.05288     -11688.73       29742.463      27277.383      44704.644      17245.361     -12460.976      368.41249      3411.4682      60877.374      41.085245      41.271957      35.901696    
         4   8166.3053     -12107.636      2735.0021     -9372.6343      66146.192      35712.019      129196.94      33529.619     -30085.085      2070.2171      13292.994      60877.374      41.085245      41.271957      35.901696    
         5   9620.794      -12167.105      3222.1293     -8944.9758      67629.984      32296.75       122837.77      47755.431     -30884.565     -5977.4926      13350.523      60877.374      41.085245      41.271957      35.901696    
         6   9685.1123     -11436.74       3243.6704     -8193.07        109268.32      90611.4        156203.82      80989.729     -60243.225     -13427.586     -19160.326      60877.374      41.085245      41.271957      35.901696    
         7   45812.539     -12237.079      15343.217      3106.1377      283541.71      500721.98      255484.83      94418.32      -265480.58     -8401.5687     -64354.292      60877.374      41.085245      41.271957      35.901696    
         8   46802.425     -10247.762      15674.742      5426.9803      381732.17      673253.91      269770.26      202172.34     -276266.64     -24708.15      -57734.097      60877.374      41.085245      41.271957      35.901696    
         9   184494.24     -12379.681      61789.525      49409.844      1094201.5      1642984.3      379364.11      1260256       -472424.67     -255159.31      251277.36      60877.374      41.085245      41.271957      35.901696    
        10   181746.98     -11660.09       60869.433      49209.342      1100529.3      1625253.9      423609.46      1252724.5     -464947.2      -251267.82      244152.47      60877.374      41.085245      41.271957      35.901696    
        11   271274.37     -12035.181      90853.322      78818.141      1635548.5      1682503.2      1942925.6      1281216.8     -603635.39     -507901.52      281308.92      60877.374      41.085245      41.271957      35.901696    
        12   274140.7      -9561.8574      91813.293      82251.435      1709140        1690243.2      2017461.8      1419715       -651027.44     -544244.31      313994.81      60877.374      41.085245      41.271957      35.901696    
        13   1461098.5     -12193.469      489340.95      477147.48      8657418.8      10219554       9855306.2      5897396.5     -8770006.6     -6420767.2      6440920.8      60877.374      41.085245      41.271957      35.901696    
        14   1450231.2     -11907.667      485701.35      473793.68      8645998.8      10146048       9781588.5      6010359.7     -8625446.9     -6257907.6      6394559.2      60877.374      41.085245      41.271957      35.901696    
        15   1546161.4     -12236.751      517829.61      505592.86      9156998.2      10335617       10001538       7133840.3     -8823892.7     -6315595.3      6675149.7      60877.374      41.085245      41.271957      35.901696    
        16   1544326.6     -7015.2794      517215.1       510199.82      9202661.5      10159189       10288056       7160739.4     -8668285.4     -6244590.8      6643501.7      60877.374      41.085245      41.271957      35.901696    
        17   17587032      -10085.224      5890126.2      5880041        1.0345792e+08  41788679       2.1212841e+08  56456679      -88404493      -1.0611976e+08  46083354       60877.374      41.085245      41.271957      35.901696    
        18   17394603      -12099.995      5825679.1      5813579.1      1.0226807e+08  43111163       2.0824587e+08  55447179      -86689971      -1.0413844e+08  45179162       60877.374      41.085245      41.271957      35.901696    
        19   17100713      -11487.247      5727251.7      5715764.5      1.0061755e+08  42696670       2.0438318e+08  54772818      -85120352      -1.0179465e+08  43974888       60877.374      41.085245      41.271957      35.901696    
ERROR: Lost atoms: original 2592 current 2587 (../thermo.cpp:481)
Last command: run             2000
njzjz commented 1 month ago

fix 1 all nve fix 2 all langevin 300 300 0.1 658845

This is an NVT simulation. But I think the output is wired.