Use DeePMD-kit¶
In this text, we will call the deep neural network that is used to represent the interatomic interactions (Deep Potential) the model. The typical procedure of using DeePMD-kit is
- Prepare data
- Train a model
- Freeze the model
- Test the model
- Compress the model
- Inference with the model
Prepare data¶
One needs to provide the following information to train a model: the atom type, the simulation box, the atom coordinate, the atom force, system energy and virial. A snapshot of a system that contains these information is called a frame. We use the following convention of units:
Property| Unit — | :—: Time | ps Length | Å Energy | eV Force | eV/Å Virial | eV Pressure| Bar
The frames of the system are stored in two formats. A raw file is a plain text file with each information item written in one file and one frame written on one line. The default files that provide box, coordinate, force, energy and virial are box.raw, coord.raw, force.raw, energy.raw and virial.raw, respectively. We recommend you use these file names. Here is an example of force.raw:
$ cat force.raw
-0.724 2.039 -0.951 0.841 -0.464 0.363
6.737 1.554 -5.587 -2.803 0.062 2.222
-1.968 -0.163 1.020 -0.225 -0.789 0.343
This force.raw contains 3 frames with each frame having the forces of 2 atoms, thus it has 3 lines and 6 columns. Each line provides all the 3 force components of 2 atoms in 1 frame. The first three numbers are the 3 force components of the first atom, while the second three numbers are the 3 force components of the second atom. The coordinate file coord.raw is organized similarly. In box.raw, the 9 components of the box vectors should be provided on each line. In virial.raw, the 9 components of the virial tensor should be provided on each line in the order XX XY XZ YX YY YZ ZX ZY ZZ. The number of lines of all raw files should be identical.
We assume that the atom types do not change in all frames. It is provided by type.raw, which has one line with the types of atoms written one by one. The atom types should be integers. For example the type.raw of a system that has 2 atoms with 0 and 1:
$ cat type.raw
0 1
Sometimes one needs to map the integer types to atom name. The mapping can be given by the file type_map.raw. For example
$ cat type_map.raw
O H
The type 0 is named by "O" and the type 1 is named by "H".
The second format is the data sets of numpy binary data that are directly used by the training program. User can use the script $deepmd_source_dir/data/raw/raw_to_set.sh to convert the prepared raw files to data sets. For example, if we have a raw file that contains 6000 frames,
$ ls
box.raw coord.raw energy.raw force.raw type.raw virial.raw
$ $deepmd_source_dir/data/raw/raw_to_set.sh 2000
nframe is 6000
nline per set is 2000
will make 3 sets
making set 0 ...
making set 1 ...
making set 2 ...
$ 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.
Train a model¶
Write the input script¶
A model has two parts, a descriptor that maps atomic configuration to a set of symmetry invariant features, and a fitting net that takes descriptor as input and predicts the atomic contribution to the target physical property.
DeePMD-kit implements the following descriptors:
se_e2_a: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.- se_e2_r: DeepPot-SE constructed from radial information of atomic configurations. The embedding takes the distance between atoms as input.
- se_e3: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes angles between two neighboring atoms as input.
loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.- hybrid: Concate a list of descriptors to form a new descriptor.
The fitting of the following physical properties are supported
enerFitting the energy of the system. The force (derivative with atom positions) and the virial (derivative with the box tensor) can also be trained. See the example.dipoleThe dipole moment.polarThe polarizability.
Training¶
The training can be invoked by
$ dp train input.json
where input.json is the name of the input script. See the example for more details.
During the training, checkpoints will be written to files with prefix save_ckpt every save_freq training steps.
Several command line options can be passed to dp train, which can be checked with
$ dp train --help
An explanation will be provided
positional arguments:
INPUT the input json database
optional arguments:
-h, --help show this help message and exit
--init-model INIT_MODEL
Initialize a model by the provided checkpoint
--restart RESTART Restart the training from the provided checkpoint
--init-model model.ckpt, initializes the model training with an existing model that is stored in the checkpoint model.ckpt, the network architectures should match.
--restart model.ckpt, continues the training from the checkpoint model.ckpt.
On some resources limited machines, one may want to control the number of threads used by DeePMD-kit. This is achieved by three environmental variables: OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS. OMP_NUM_THREADS controls the multithreading of DeePMD-kit implemented operations. TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS controls intra_op_parallelism_threads and inter_op_parallelism_threads, which are Tensorflow configurations for multithreading. An explanation is found here.
For example if you wish to use 3 cores of 2 CPUs on one node, you may set the environmental variables and run DeePMD-kit as follows:
export OMP_NUM_THREADS=6
export TF_INTRA_OP_PARALLELISM_THREADS=3
export TF_INTER_OP_PARALLELISM_THREADS=2
dp train input.json
Freeze a model¶
The trained neural network is extracted from a checkpoint and dumped into a database. This process is called “freezing” a model. The idea and part of our code are from Morgan. To freeze a model, typically one does
$ dp freeze -o graph.pb
in the folder where the model is trained. The output database is called graph.pb.
Test a model¶
The frozen model can be used in many ways. The most straightforward test can be performed using dp test. A typical usage of dp test is
dp test -m graph.pb -s /path/to/system -n 30
where -m gives the tested model, -s the path to the tested system and -n the number of tested frames. Several other command line options can be passed to dp test, which can be checked with
$ dp test --help
An explanation will be provided
usage: dp test [-h] [-m MODEL] [-s SYSTEM] [-S SET_PREFIX] [-n NUMB_TEST]
[-r RAND_SEED] [--shuffle-test] [-d DETAIL_FILE]
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Frozen model file to import
-s SYSTEM, --system SYSTEM
The system dir
-S SET_PREFIX, --set-prefix SET_PREFIX
The set prefix
-n NUMB_TEST, --numb-test NUMB_TEST
The number of data for test
-r RAND_SEED, --rand-seed RAND_SEED
The random seed
--shuffle-test Shuffle test data
-d DETAIL_FILE, --detail-file DETAIL_FILE
The file containing details of energy force and virial
accuracy
Compress a model¶
Once the frozen model is obtained from deepmd-kit, we can get the neural network structure and its parameters (weights, biases, etc.) from the trained model, and compress it in the following way:
dp compress input.json -i graph.pb -o graph-compress.pb
where input.json denotes the original training input script, -i gives the original frozen model, -o gives the compressed model. Several other command line options can be passed to dp compress, which can be checked with
$ dp compress --help
An explanation will be provided
usage: dp compress [-h] [-i INPUT] [-o OUTPUT] [-e EXTRAPOLATE] [-s STRIDE]
[-f FREQUENCY] [-d FOLDER]
INPUT
positional arguments:
INPUT The input parameter file in json or yaml format, which
should be consistent with the original model parameter
file
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
The original frozen model, which will be compressed by
the deepmd-kit
-o OUTPUT, --output OUTPUT
The compressed model
-e EXTRAPOLATE, --extrapolate EXTRAPOLATE
The scale of model extrapolation
-s STRIDE, --stride STRIDE
The uniform stride of tabulation's first table, the
second table will use 10 * stride as it's uniform
stride
-f FREQUENCY, --frequency FREQUENCY
The frequency of tabulation overflow check(If the
input environment matrix overflow the first or second
table range). By default do not check the overflow
-d FOLDER, --folder FOLDER
path to checkpoint folder
Parameter explanation
Model compression, which including tabulating the embedding-net. The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first sub-table takes the stride(parameter) as it’s uniform stride, while the second sub-table takes 10 * stride as it’s uniform stride. The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper. Finally, we added a check frequency parameter. It indicates how often the program checks for overflow(if the input environment matrix overflow the first or second table range) during the MD inference.
Justification of model compression
Model compression, with little loss of accuracy, can greatly speed up MD inference time. According to different simulation systems and training parameters, the speedup can reach more than 10 times at both CPU and GPU devices. At the same time, model compression can greatly change the memory usage, reducing as much as 20 times under the same hardware conditions.
Acceptable original model version
The model compression method requires that the version of DeePMD-kit used in original model generation should be 1.3 or above. If one has a frozen 1.2 model, one can first use the convenient conversion interface of DeePMD-kit-v1.2.4 to get a 1.3 executable model.(eg: dp convert-to-1.3 -i frozen_1.2.pb -o frozen_1.3.pb)
Model inference¶
One may use the python interface of DeePMD-kit for model inference, an example is given as follows
from deepmd.infer import DeepPot
import numpy as np
dp = DeepPot('graph.pb')
coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1,0,1]
e, f, v = dp.eval(coord, cell, atype)
where e, f and v are predicted energy, force and virial of the system, respectively.
Run MD with LAMMPS¶
Include deepmd in the pair style¶
Running an MD simulation with LAMMPS is simpler. In the LAMMPS input file, one needs to specify the pair style as follows
pair_style deepmd graph.pb
pair_coeff
where graph.pb is the file name of the frozen model. The pair_coeff should be left blank. It should be noted that LAMMPS counts atom types starting from 1, therefore, all LAMMPS atom type will be firstly subtracted by 1, and then passed into the DeePMD-kit engine to compute the interactions. A detailed documentation of this pair style is available..
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.
Run path-integral MD with i-PI¶
The i-PI works in a client-server model. The i-PI provides the server for integrating the replica positions of atoms, while the DeePMD-kit provides a client named dp_ipi that computes the interactions (including energy, force and virial). The server and client communicates via the Unix domain socket or the Internet socket. The client can be started by
$ dp_ipi water.json
It is noted that multiple instances of the client is allow for computing, in parallel, the interactions of multiple replica of the path-integral MD.
water.json is the parameter file for the client dp_ipi, and an example is provided:
{
"verbose": false,
"use_unix": true,
"port": 31415,
"host": "localhost",
"graph_file": "graph.pb",
"coord_file": "conf.xyz",
"atom_type" : {
"OW": 0,
"HW1": 1,
"HW2": 1
}
}
The option use_unix is set to true to activate the Unix domain socket, otherwise, the Internet socket is used.
The option graph_file provides the file name of the frozen model.
The dp_ipi gets the atom names from an XYZ file provided by coord_file (meanwhile ignores all coordinates in it), and translates the names to atom types by rules provided by atom_type.
Use deep potential with ASE¶
Deep potential can be set up as a calculator with ASE to obtain potential energies and forces.
from ase import Atoms
from deepmd.calculator import DP
water = Atoms('H2O',
positions=[(0.7601, 1.9270, 1),
(1.9575, 1, 1),
(1., 1., 1.)],
cell=[100, 100, 100],
calculator=DP(model="frozen_model.pb"))
print(water.get_potential_energy())
print(water.get_forces())
Optimization is also available:
from ase.optimize import BFGS
dyn = BFGS(water)
dyn.run(fmax=1e-6)
print(water.get_positions())