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LAMMPS commands

Enable DeePMD-kit plugin (plugin mode)

If you are using the plugin mode, enable DeePMD-kit package in LAMMPS with plugin command:

plugin load libdeepmd_lmp.so

After LAMMPS version patch_24Mar2022, another way to load plugins is to set the environmental variable LAMMPS_PLUGIN_PATH:

LAMMPS_PLUGIN_PATH=$deepmd_root/lib/deepmd_lmp

where $deepmd_root is the directory to install C++ interface.

The built-in mode doesn't need this step.

pair_style deepmd

The DeePMD-kit package provides the pair_style deepmd

pair_style deepmd models ... keyword value ...
  • deepmd = style of this pair_style
  • models = frozen model(s) to compute the interaction. If multiple models are provided, then only the first model serves to provide energy and force prediction for each timestep of molecular dynamics, and the model deviation will be computed among all models every out_freq timesteps.
  • keyword = out_file or out_freq or fparam or fparam_from_compute or atomic or relative or relative_v or aparam or ttm
    out_file value = filename
        filename = The file name for the model deviation output. Default is model_devi.out
    out_freq value = freq
        freq = Frequency for the model deviation output. Default is 100.
    fparam value = parameters
        parameters = one or more frame parameters required for model evaluation.
    fparam_from_compute value = id
        id = compute id used to update the frame parameter.
    atomic = no value is required.
        If this keyword is set, the model deviation of each atom will be output.
    relative value = level
        level = The level parameter for computing the relative model deviation of the force
    relative_v value = level
        level = The level parameter for computing the relative model deviation of the virial
    aparam value = parameters
        parameters = one or more atomic parameters of each atom required for model evaluation
    ttm value = id
        id = fix ID of fix ttm

Examples

pair_style deepmd graph.pb
pair_style deepmd graph.pb fparam 1.2
pair_style deepmd graph_0.pb graph_1.pb graph_2.pb out_file md.out out_freq 10 atomic relative 1.0
pair_coeff * * O H

pair_style deepmd cp.pb fparam_from_compute TEMP
compute    TEMP all temp

Description

Evaluate the interaction of the system by using Deep Potential or Deep Potential Smooth Edition. It is noticed that deep potential is not a "pairwise" interaction, but a multi-body interaction.

This pair style takes the deep potential defined in a model file that usually has the .pb extension. The model can be trained and frozen by package DeePMD-kit.

The model deviation evalulates the consistency of the force predictions from multiple models. By default, only the maximal, minimal and average model deviations are output. If the key atomic is set, then the model deviation of force prediction of each atom will be output.

By default, the model deviation is output in absolute value. If the keyword relative is set, then the relative model deviation of the force will be output, including values output by the keyword atomic. The relative model deviation of the force on atom $i$ is defined by

$$E_{f_i}=\frac{\left|D_{f_i}\right|}{\left|f_i\right|+l}$$

where $D_{f_i}$ is the absolute model deviation of the force on atom $i$, $f_i$ is the norm of the force and $l$ is provided as the parameter of the keyword relative. If the keyword relative_v is set, then the relative model deviation of the virial will be output instead of the absolute value, with the same definition of that of the force:

$$E_{v_i}=\frac{\left|D_{v_i}\right|}{\left|v_i\right|+l}$$

If the keyword fparam is set, the given frame parameter(s) will be fed to the model. If the keyword fparam_from_compute is set, the global parameter(s) from compute command (e.g., temperature from compute temp command) will be fed to the model as the frame parameter(s). If the keyword aparam is set, the given atomic parameter(s) will be fed to the model, where each atom is assumed to have the same atomic parameter(s). If the keyword ttm is set, electronic temperatures from fix ttm command will be fed to the model as the atomic parameters.

Only a single pair_coeff command is used with the deepmd style which specifies atom names. These are mapped to LAMMPS atom types (integers from 1 to Ntypes) by specifying Ntypes additional arguments after * * in the pair_coeff command. If atom names are not set in the pair_coeff command, the training parameter {ref}type_map <model/type_map> will be used by default. If the training parameter {ref}type_map <model/type_map> is not set, atom names in the pair_coeff command cannot be set. In this case, atom type indexes in type.raw (integers from 0 to Ntypes-1) will map to LAMMPS atom types.

Restrictions

  • The deepmd pair style is provided in the USER-DEEPMD package, which is compiled from the DeePMD-kit, visit the DeePMD-kit website for more information.

Compute tensorial properties

The DeePMD-kit package provides the compute deeptensor/atom for computing atomic tensorial properties.

compute ID group-ID deeptensor/atom model_file
  • ID: user-assigned name of the computation
  • group-ID: ID of the group of atoms to compute
  • deeptensor/atom: the style of this compute
  • model_file: the name of the binary model file.

At this time, the training parameter {ref}type_map <model/type_map> will be mapped to LAMMPS atom types.

Examples

compute         dipole all deeptensor/atom dipole.pb

The result of the compute can be dumped to trajectory file by

dump            1 all custom 100 water.dump id type c_dipole[1] c_dipole[2] c_dipole[3]

Restrictions

  • The deeptensor/atom compute is provided in the USER-DEEPMD package, which is compiled from the DeePMD-kit, visit the DeePMD-kit website for more information.

Long-range interaction

The reciprocal space part of the long-range interaction can be calculated by LAMMPS command kspace_style. To use it with DeePMD-kit, one writes

pair_style	deepmd graph.pb
pair_coeff  * *
kspace_style	pppm 1.0e-5
kspace_modify	gewald 0.45

Please notice that the DeePMD does nothing to the direct space part of the electrostatic interaction, because this part is assumed to be fitted in the DeePMD model (the direct space cut-off is thus the cut-off of the DeePMD model). The splitting parameter gewald is modified by the kspace_modify command.

Use of the centroid/stress/atom to get the full 3x3 "atomic-virial"

The DeePMD-kit allows also the computation of per-atom stress tensor defined as:

$$dvatom=\sum_{m}( \mathbf{r}_n- \mathbf{r}_m) \frac{de_m}{d\mathbf{r}_n}$$

Where $\mathbf{r}_n$ is the atomic position of nth atom, $\mathbf{v}_n$ velocity of the atom and $\frac{de_m}{d\mathbf{r}_n}$ the derivative of the atomic energy.

In LAMMPS one can get the per-atom stress using the command centroid/stress/atom:

compute ID group-ID centroid/stress/atom NULL virial

see LAMMPS doc page for more details on the meaning of the keywords.

Examples

In order of computing the 9-component per-atom stress

compute stress all centroid/stress/atom NULL virial

Thus c_stress is an array with 9 components in the order xx,yy,zz,xy,xz,yz,yx,zx,zy.

If you use this feature please cite D. Tisi, L. Zhang, R. Bertossa, H. Wang, R. Car, S. Baroni - arXiv preprint arXiv:2108.10850, 2021

Computation of heat flux

Using a per-atom stress tensor one can, for example, compute the heat flux defined as:

$$\mathbf J = \sum_n e_n \mathbf v_n + \sum_{n,m} ( \mathbf r_m- \mathbf r_n) \frac{de_m}{d\mathbf r_n} \mathbf v_n$$

to compute the heat flux with LAMMPS:

compute ke_ID all ke/atom
compute pe_ID all pe/atom
compute stress_ID group-ID centroid/stress/atom NULL virial
compute flux_ID all heat/flux ke_ID pe_ID stress_ID

Examples

compute ke all ke/atom
compute pe all pe/atom
compute stress all centroid/stress/atom NULL virial
compute flux all heat/flux ke pe stress

c_flux is a global vector of length 6. The first three components are the $x$, $y$ and $z$ components of the full heat flux vector. The others are the components of the so-called convective portion, see LAMMPS doc page for more detailes.

If you use these features please cite D. Tisi, L. Zhang, R. Bertossa, H. Wang, R. Car, S. Baroni - arXiv preprint arXiv:2108.10850, 2021