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DeePMD-kit Manual

Table of contents

About DeePMD-kit

DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.

Highlighted features

interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient.

interfaced with high-performance classical MD and quantum (path-integral) MD packages, i.e., LAMMPS and i-PI, respectively.

implements the Deep Potential series models, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc.

implements MPI and GPU supports, makes it highly efficient for high performance parallel and distributed computing.

highly modularized, easy to adapt to different descriptors for deep learning based potential energy models.

Code structure

The code is organized as follows:

data/raw: tools manipulating the raw data files.

examples: example json parameter files.

source/3rdparty: third-party packages used by DeePMD-kit.

source/cmake: cmake scripts for building.

source/ipi: source code of i-PI client.

source/lib: source code of DeePMD-kit library.

source/lmp: source code of Lammps module.

source/op: tensorflow op implementation. working with library.

source/scripts: Python script for model freezing.

source/train: Python modules and scripts for training and testing.

License and credits

The project DeePMD-kit is licensed under GNU LGPLv3.0.

If you use this code in any future publications, please cite this using

Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.

Deep Potential in a nutshell

The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called atomic energy. Summing up all the atomic energies gives the potential energy of the system.

The initial proof of concept is in the Deep Potential paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical ab initio molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics (DeePMD) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations.

Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition (DeepPot-SE) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science.

In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the DeePCG paper for more details.

Download and install

Please follow our github webpage to see the latest released version and development version.

Easy installation methods

There various easy methods to install DeePMD-kit. Choose one that you prefer. If you want to build by yourself, jump to the next two sections.

With Docker

A docker for installing the DeePMD-kit on CentOS 7 is available here.

With conda

DeePMD-kit is avaiable with conda. Install Anaconda or Miniconda first.

To install the CPU version:

conda installdeepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.0:

conda installdeepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

Offline packages

Both CPU and GPU version offline package are avaiable in the Releases page.

Install the python interface

Install the Tensorflow's python interface

First, check the python version on your machine

python --version

We follow the virtual environment approach to install the tensorflow's Python interface. The full instruction can be found on the tensorflow's official website. Now we assume that the Python interface will be installed to virtual environment directory $tensorflow_venv

virtualenv -p python3 $tensorflow_venv

source $tensorflow_venv/bin/activate

pip install --upgrade pip

pip install --upgrade tensorflow==2.1.0

It is notice that everytime a new shell is started and one wants to use DeePMD-kit, the virtual environment should be activated by

source $tensorflow_venv/bin/activate

if one wants to skip out of the virtual environment, he/she can do

deactivate

If one has multiple python interpreters named like python3.x, it can be specified by, for example

virtualenv -p python3.7 $tensorflow_venv

If one does not need the GPU support of deepmd-kit and is concerned about package size, the CPU-only version of tensorflow should be installed by

pip install --upgrade tensorflow-cpu==2.1.0

To verify the installation, run

python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

One should remember to activate the virtual environment every time he/she uses deepmd-kit.

Install the DeePMD-kit's python interface

Execute

pip installdeepmd-kit

To test the installation, one may execute

dp -h

It will print the help information like

usage: dp [-h] {train,freeze,test} ...

DeePMD-kit: A deep learning package for many-body potential energy

representation and molecular dynamics

optional arguments:

-h, --help show this help message and exit

Valid subcommands:

{train,freeze,test}

train train a model

freeze freeze the model

test test the model

Install the C++ interface

If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python interface installed in the previous section does everything and he/she can safely skip this section.

Install the Tensorflow's C++ interface

Check the compiler version on your machine

gcc --version

The C++ interface of DeePMD-kit was tested with compiler gcc >= 4.8. It is noticed that the I-Pi support is only compiled with gcc >= 4.9.

First the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be in consistent with the python interface. We assume that you have followed our instruction and installed tensorflow python interface 1.14.0 with, then you may follow the instruction for CPU to install the corresponding C++ interface (CPU only). If one wants GPU supports, he/she should follow the instruction for GPU to install the C++ interface.

Install the DeePMD-kit's C++ interface

Clone the DeePMD-kit source code

cd /some/workspace

git clone --recursive https://github.com/deepmodeling/deepmd-kit.git deepmd-kit

For convenience, you may want to record the location of source to a variable, saying deepmd_source_dir by

cddeepmd-kit

deepmd_source_dir=`pwd`

Now goto the source code directory of DeePMD-kit and make a build place.

cd $deepmd_source_dir/source

mkdirbuild

cdbuild

I assume you want to install DeePMD-kit into path $deepmd_root, then execute cmake

cmake -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..

where the variable tensorflow_root stores the location where the tensorflow's C++ interface is installed. The DeePMD-kit will automatically detect if a CUDA tool-kit is available on your machine and build the GPU support accordingly. If you want to force the cmake to find CUDA tool-kit, you can speicify the key USE_CUDA_TOOLKIT,

cmake -DUSE_CUDA_TOOLKIT=true -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..

and you may further asked to provide CUDA_TOOLKIT_ROOT_DIR. If the cmake has executed successfully, then

make

make install

If everything works fine, you will have the following executable and libraries installed in $deepmd_root/bin and $deepmd_root/lib

$ls $deepmd_root/bin

dp_ipi

$ls $deepmd_root/lib

libdeepmd_ipi.so libdeepmd_op.so libdeepmd.so

Install LAMMPS's DeePMD-kit module

DeePMD-kit provide module for running MD simulation with LAMMPS. Now make the DeePMD-kit module for LAMMPS.

cd $deepmd_source_dir/source/build

make lammps

DeePMD-kit will generate a module called USER-DEEPMD in the build directory. Now download your favorite LAMMPS code, and uncompress it (I assume that you have downloaded the tar lammps-stable.tar.gz)

cd /some/workspace

tarxf lammps-stable.tar.gz

The source code of LAMMPS is stored in directory, for example lammps-31Mar17. Now go into the LAMMPS code and copy the DeePMD-kit module like this

cdlammps-31Mar17/src/

cp -r $deepmd_source_dir/source/build/USER-DEEPMD .

Now build LAMMPS

make yes-user-deepmd

make mpi -j4

The option -j4 means using 4 processes in parallel. You may want to use a different number according to your hardware.

If everything works fine, you will end up with an executable lmp_mpi.

The DeePMD-kit module can be removed from LAMMPS source code by

make no-user-deepmd

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

MD runs with the model (Native MD code or LAMMPS)

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

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:

$catforce.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. 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:

$cattype.raw

0 1

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 setis 2000

will make 3 sets

making set0 ...

making set1 ...

making set2 ...

$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. The last set (set.002) is used as testing set, while the rest sets (set.000 and set.001) are used as training sets. 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

The method of training is explained in our DeePMD and DeepPot-SE papers. With the source code we provide a small training dataset taken from 400 frames generated by NVT ab-initio water MD trajectory with 300 frames for training and 100 for testing. An example training parameter file is provided. One can try with the training by

$cd $deepmd_source_dir/examples/water/train/

$dp train water_se_a.json

where water_se_a.json is the json format parameter file that controls the training. The components of the water.json contains three parts, model, learning_rate, loss and training.

The model section specify how the deep potential model is built. An example of the smooth-edition is provided as follows

"model":{

"type_map":["O","H"],

"descriptor":{

"type":"se_a",

"rcut_smth":5.80,

"rcut":6.00,

"sel":[46,92],

"neuron":[25,50,100],

"axis_neuron":16,

"resnet_dt":false,

"seed":1,

"_comment":" that's all"

},

"fitting_net":{

"neuron":[240,240,240],

"resnet_dt":true,

"seed":1,

"_comment":" that's all"

},

"_comment":" that's all"

}

The type_map is optional, which provide the element names (but not restricted to) for corresponding atom types.

The construction of the descriptor is given by option descriptor. The type of the descriptor is set to "se_a", which means smooth-edition, angular infomation. The rcut is the cut-off radius for neighbor searching, and the rcut_smth gives where the smoothing starts. sel gives the maximum possible number of neighbors in the cut-off radius. It is a list, the length of which is the same as the number of atom types in the system, and sel[i] denote the maximum possible number of neighbors with type i. The neuron specifies the size of the embedding net. From left to right the members denote the sizes of each hidden layers from input end to the output end, respectively. The axis_neuron specifies the size of submatrix of the embedding matrix, the axis matrix as explained in the DeepPot-SE paper. If the outer layer is of twice size as the inner layer, then the inner layer is copied and concatenated, then a ResNet architecture is build between them. If the option resnet_dt is set true, then a timestep is used in the ResNet. seed gives the random seed that is used to generate random numbers when initializing the model parameters.

The construction of the fitting net is give by fitting_net. The key neuron specifies the size of the fitting net. If two neighboring layers are of the same size, then a ResNet architecture is build between them. If the option resnet_dt is set true, then a timestep is used in the ResNet. seed gives the random seed that is used to generate random numbers when initializing the model parameters.

An example of the learning_rate is given as follows

"learning_rate":{

"type":"exp",

"start_lr":0.005,

"decay_steps":5000,

"decay_rate":0.95,

"_comment":"that's all"

}

The option start_lr, decay_rate and decay_steps specify how the learning rate changes. For example, the tth batch will be trained with learning rate:

lr(t) = start_lr * decay_rate ^ ( t / decay_steps )

An example of the loss is

"loss":{

"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,

"_comment":" that's all"

}

The options start_pref_e, limit_pref_e, start_pref_f, limit_pref_f, start_pref_v and limit_pref_v determine how the prefactors of energy error, force error and virial error changes in the loss function (see the appendix of the DeePMD paper for details). Taking the prefactor of force error for example, the prefactor at batch t is

w_f(t) = start_pref_f * ( lr(t) / start_lr ) + limit_pref_f * ( 1 - lr(t) / start_lr )

Since we do not have virial data, the virial prefactors start_pref_v and limit_pref_v are set to 0.

An example of training is

"training":{

"systems":["../data/"],

"set_prefix":"set",

"stop_batch":1000000,

"batch_size":1,

"seed":1,

"_comment":" display and restart",

"_comment":" frequencies counted in batch",

"disp_file":"lcurve.out",

"disp_freq":100,

"numb_test":10,

"save_freq":1000,

"save_ckpt":"model.ckpt",

"load_ckpt":"model.ckpt",

"disp_training":true,

"time_training":true,

"profiling":false,

"profiling_file":"timeline.json",

"_comment":"that's all"

}

The option systems provide location of the systems (path to set.* and type.raw). It is a vector, thus DeePMD-kit allows you to provide multiple systems. DeePMD-kit will train the model with the systems in the vector one by one in a cyclic manner. It is warned that the example water data (in folder examples/data/water) is of very limited amount, is provided only for testing purpose, and should not be used to train a productive model.

The option batch_size specifies the number of frames in each batch. It can be set to "auto" to enable a automatic batch size.

The option stop_batch specifies the total number of batches will be used in the training.

Training

The training can be invoked by

$dp train water_se_a.json

During the training, the error of the model is tested every disp_freq batches with numb_test frames from the last set in the systems directory on the fly, and the results are output to disp_file. A typical disp_file looks like

# batch l2_tst l2_trn l2_e_tst l2_e_trn l2_f_tst l2_f_trn lr

0 2.67e+01 2.57e+01 2.21e-01 2.22e-01 8.44e-01 8.12e-01 1.0e-03

100 6.14e+00 5.40e+00 3.01e-01 2.99e-01 1.93e-01 1.70e-01 1.0e-03

200 5.02e+00 4.49e+00 1.53e-01 1.53e-01 1.58e-01 1.42e-01 1.0e-03

300 4.36e+00 3.71e+00 7.32e-02 7.27e-02 1.38e-01 1.17e-01 1.0e-03

400 4.04e+00 3.29e+00 3.16e-02 3.22e-02 1.28e-01 1.04e-01 1.0e-03

The first column displays the number of batches. The second and third columns display the loss function evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The fourth and fifth columns display the RMS energy error (normalized by number of atoms) evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The sixth and seventh columns display the RMS force error (component-wise) evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The last column displays the current learning rate.

Checkpoints will be written to files with prefix save_ckpt every save_freq batches. If restart is set to true, then the training will start from the checkpoint named load_ckpt, rather than from scratch.

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

The keys intra_op_parallelism_threads and inter_op_parallelism_threads are Tensorflow configurations for multithreading, which are explained here. Skipping -t and OMP_NUM_THREADS leads to the default setting of these keys in the Tensorflow.

--init-model model.ckpt, for example, 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:

exportOMP_NUM_THREADS=6

exportTF_INTRA_OP_PARALLELISM_THREADS=3

exportTF_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

Model inference

One may use the python interface of DeePMD-kit for model inference, an example is given as follows

import deepmd.DeepPot as DP

import numpy as np

dp = DP('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_styledeepmd graph.pb

pair_coeff

kspace_stylepppm 1.0e-5

kspace_modifygewald 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())

Troubleshooting

In consequence of various differences of computers or systems, problems may occur. Some common circumstances are listed as follows.

If other unexpected problems occur, you're welcome to contact us for help.

Model compatability

When the version of DeePMD-kit used to training model is different from the that of DeePMD-kit running MDs, one has the problem of model compatability.

DeePMD-kit guarantees that the codes with the same major and minor revisions are compatible. That is to say v0.12.5 is compatible to v0.12.0, but is not compatible to v0.11.0 nor v1.0.0.

Installation: inadequate versions of gcc/g++

Sometimes you may use a gcc/g++ of version <4.9. If you have a gcc/g++ of version > 4.9, say, 7.2.0, you may choose to use it by doing

exportCC=/path/to/gcc-7.2.0/bin/gcc

exportCXX=/path/to/gcc-7.2.0/bin/g++

If, for any reason, for example, you only have a gcc/g++ of version 4.8.5, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit. i-Pi will be disabled automatically.

Installation: build files left in DeePMD-kit

When you try to build a second time when installing DeePMD-kit, files produced before may contribute to failure. Thus, you may clear them by

cdbuild

rm -r *

and redo the cmake process.

MD: cannot run LAMMPS after installing a new version of DeePMD-kit

This typically happens when you install a new version of DeePMD-kit and copy directly the generated USER-DEEPMD to a LAMMPS source code folder and re-install LAMMPS.

To solve this problem, it suffices to first remove USER-DEEPMD from LAMMPS source code by

make no-user-deepmd

and then install the new USER-DEEPMD.

If this does not solve your problem, try to decompress the LAMMPS source tarball and install LAMMPS from scratch again, which typically should be very fast.