3.14. Deep potential long-range (DPLR)

Notice: The interfaces of DPLR are not stable and subject to change

The method of DPLR is described in this paper. One is recommended to read the paper before using the DPLR.

In the following, we take the DPLR model for example to introduce the training and LAMMPS simulation with the DPLR model. The DPLR model is trained in two steps.

3.14.1. Theory

The Deep Potential Long Range (DPLR) model adds the electrostatic energy to the total energy:

\[ E=E_{\text{DP}} + E_{\text{ele}},\]

where \(E_{\text{DP}}\) is the short-range contribution constructed as the standard energy model that is fitted against \((E^\ast-E_{\text{ele}})\). \(E_{\text{ele}}\) is the electrostatic energy introduced by a group of Gaussian distributions that is an approximation of the electronic structure of the system, and is calculated in Fourier space by

\[ E_{\text{ele}} = \frac{1}{2\pi V}\sum_{m \neq 0, \|m\|\leq L} \frac{\exp({-\pi ^2 m^2/\beta ^2})}{m^2}S^2(m),\]

where \(\beta\) is a freely tunable parameter that controls the spread of the Gaussians. \(L\) is the cutoff in Fourier space and \(S(m)\), the structure factor, is given by

\[ S(m)=\sum_i q_i e^{-2\pi \imath m \boldsymbol r_i} + \sum_n q_n e^{-2\pi \imath m \boldsymbol W_n},\]

where \(\imath = \sqrt{-1}\) denotes the imaginary unit, \(\boldsymbol r_i\) indicates ion coordinates, \(q_i\) is the charge of the ion \(i\), and \(W_n\) is the \(n\)-th Wannier centroid (WC) which can be obtained from a separated dipole model. It can be proved that the error in the electrostatic energy introduced by the Gaussian approximations is dominated by a summation of dipole-quadrupole interactions that decay as \(r^{-4}\), where \(r\) is the distance between the dipole and quadrupole.1

3.14.2. Train a deep Wannier model for Wannier centroids

We use the deep Wannier model (DW) to represent the relative position of the Wannier centroid (WC) with the atom with which it is associated. One may consult the introduction of the dipole model for a detailed introduction. An example input wc.json and a small dataset data for tutorial purposes can be found in


It is noted that the tutorial dataset is not enough for training a productive model. Two settings make the training input script different from an energy training input:

	"fitting_net": {
	    "type":		"dipole",
	    "dipole_type":	[0],
	    "neuron":		[128, 128, 128],
	    "seed":		1

The type of fitting is set to dipole. The dipole is associated with type 0 atoms (oxygens), by the setting "dipole_type": [0]. What we trained is the displacement of the WC from the corresponding oxygen atom. It shares the same training input as the atomic dipole because both are 3-dimensional vectors defined on atoms. The loss section is provided as follows

    "loss": {
	"type":		"tensor",
	"pref":		0.0,
	"pref_atomic":	1.0

so that the atomic dipole is trained as labels. Note that the NumPy compressed file atomic_dipole.npy should be provided in each dataset.

The training and freezing can be started from the example directory by

dp train dw.json && dp freeze -o dw.pb

3.14.3. Train the DPLR model

The training of the DPLR model is very similar to the standard short-range DP models. An example input script can be found in the example directory. The following section is introduced to compute the long-range energy contribution of the DPLR model, and modify the short-range DP model by this part.

        "modifier": {
            "type":             "dipole_charge",
            "model_name":       "dw.pb",
            "model_charge_map": [-8],
            "sys_charge_map":   [6, 1],
            "ewald_h":          1.00,
            "ewald_beta":       0.40

The model_name specifies which DW model is used to predict the position of WCs. model_charge_map gives the amount of charge assigned to WCs. sys_charge_map provides the nuclear charge of oxygen (type 0) and hydrogen (type 1) atoms. ewald_beta (unit \(\text{Å}^{-1}\)) gives the spread parameter controls the spread of Gaussian charges, and ewald_h (unit Å) assigns the grid size of Fourier transformation. The DPLR model can be trained and frozen by (from the example directory)

dp train ener.json && dp freeze -o ener.pb

3.14.4. Molecular dynamics simulation with DPLR

In MD simulations, the long-range part of the DPLR is calculated by the LAMMPS kspace support. Then the long-range interaction is back-propagated to atoms by DeePMD-kit. This setup is commonly used in classical molecular dynamics simulations as the “virtual site”. Unfortunately, LAMMPS does not natively support virtual sites, so we have to hack the LAMMPS code, which makes the input configuration and script a little wired.

An example of an input configuration file and script can be found in


We use atom_style full for DPLR simulations. the coordinates of the WCs are explicitly written in the configuration file. Moreover, a virtual bond is established between the oxygens and the WCs to indicate they are associated together. The configuration file containing 128 H2O molecules is thus written as

512 atoms
3 atom types
128 bonds
1 bond types

0 16.421037674 xlo xhi
0 16.421037674 ylo yhi
0 16.421037674 zlo zhi
0 0 0 xy xz yz


1 16
2 2
3 16


       1        1 1  6 8.4960699081e+00 7.5073699951e+00 9.6371297836e+00
       2        2 1  6 4.0597701073e+00 6.8156299591e+00 1.2051420212e+01

     385        1 3 -8 8.4960699081e+00 7.5073699951e+00 9.6371297836e+00
     386        2 3 -8 4.0597701073e+00 6.8156299591e+00 1.2051420212e+01


1 1 1 385
2 1 2 386

The oxygens and hydrogens are assigned with atom types 1 and 2 (corresponding to training atom types 0 and 1), respectively. The WCs are assigned with atom type 3. We want to simulate heavy water so the mass of hydrogens is set to 2.

An example input script is provided in


Here are some explanations

# groups of real and virtual atoms
group           real_atom type 1 2
group           virtual_atom type 3

# bond between real and its corresponding virtual site should be given
# to setup a map between real and virtual atoms. However, no real
# bonded interaction is applied, thus bond_sytle "zero" is used.
pair_style      deepmd ener.pb
pair_coeff      * *
bond_style      zero
bond_coeff      *
special_bonds   lj/coul 1 1 1 angle no

Type 1 and 2 (O and H) are real_atoms, while type 3 (WCs) are virtual_atoms. The model file ener.pb stores both the DW and DPLR models, so the position of WCs and the energy can be inferred from it. A virtual bond type is specified by bond_style zero. The special_bonds command switches off the exclusion of intramolecular interactions.

# kspace_style "pppm/dplr" should be used. in addition the
# gewald(1/distance) should be set the same as that used in
# training. Currently only ik differentiation is supported.
kspace_style	pppm/dplr 1e-5
kspace_modify	gewald ${BETA} diff ik mesh ${KMESH} ${KMESH} ${KMESH}

The long-range part is calculated by the kspace support of LAMMPS. The kspace_style pppm/dplr is required. The spread parameter set by variable BETA should be set the same as that used in training. The KMESH should be set dense enough so the long-range calculation is converged. fix dplr command


fix ID group-ID style_name keyword value ...
  • ID, group-ID are documented in :doc:fix <fix> command

  • style_name = dplr

  • three or more keyword/value pairs may be appended

keyword = *model* or *type_associate* or *bond_type* or *efield*
  *model* value = name
    name = name of DPLR model file (e.g. frozen_model.pb) (not DW model)
  *type_associate* values = NR1 NW1 NR2 NW2 ...
    NRi = type of real atom in i-th (real atom, Wannier centroid) pair
    NWi = type of Wannier in i-th (real atom, Wannier centroid) pair
  *bond_type* values = NB1 NB2 ...
    NBi = bond type of i-th (real atom, Wannier centroid) pair
  *efield* (optional) values = Ex Ey Ez
    Ex/Ey/Ez = electric field along x/y/z direction


# "fix dplr" set the position of the virtual atom, and spread the
# electrostatic interaction asserting on the virtual atom to the real
# atoms. "type_associate" associates the real atom type its
# corresponding virtual atom type. "bond_type" gives the type of the
# bond between the real and virtual atoms.
fix		0 all dplr model ener.pb type_associate 1 3 bond_type 1
fix_modify	0 virial yes

The fix command dplr calculates the position of WCs by the DW model and back-propagates the long-range interaction on virtual atoms to real toms. The atom names specified in pair_style deepmd will be used to determine elements. If it is not set, the training parameter type_map will be mapped to LAMMPS atom types.

To use a time-dependent electric field, LAMMPS’s variable feature can be utilized:

variable EFIELD_Z equal 2*sin(2*PI*time/0.006)
fix 0 all dplr model ener.pb type_associate 1 3 bond_type 1 efield 0 0 v_EFIELD_Z
fix_modify 0 energy yes virial yes

The efield feature of fix dplr behaves similarly to LAMMPS’s fix efield. Note that the atomic energy or potential in fix efield is not yet supported in fix dplr. For a detailed description on how a time-dependent variable can be defined, refer to LAMMPS’s document of variable.

# compute the temperature of real atoms, excluding virtual atom contribution
compute		real_temp real_atom temp
compute		real_press all pressure real_temp
fix		1 real_atom nvt temp ${TEMP} ${TEMP} ${TAU_T}
fix_modify	1 temp real_temp

The temperature of the system should be computed from the real atoms. The kinetic contribution in the pressure tensor is also computed from the real atoms. The thermostat is applied to only real atoms. The computed temperature and pressure of real atoms can be accessed by, e.g.

fix             thermo_print all print ${THERMO_FREQ} "$(step) $(pe) $(ke) $(etotal) $(enthalpy) $(c_real_temp) $(c_real_press) $(vol) $(c_real_press[1]) $(c_real_press[2]) $(c_real_press[3])" append thermo.out screen no title "# step pe ke etotal enthalpy temp press vol pxx pyy pzz"

The LAMMPS simulation can be started from the example directory by

lmp -i in.lammps

If LAMMPS complains that no model file ener.pb exists, it can be copied from the training example directory.

The MD simulation lasts for only 20 steps. If one runs a longer simulation, it will blow up, because the model is trained with a very limited dataset for very short training steps, thus is of poor quality.

Another restriction that should be noted is that the energies printed at the zero steps are not correct. This is because at the zero steps the position of the WC has not been updated with the DW model. The energies printed in later steps are correct.


This section is built upon Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li’ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang, Han Wang, J. Chem. Phys. 159, 054801 (2023) licensed under a Creative Commons Attribution (CC BY) license.