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A script to make molecular dynamics (MD) datasets for neural networks from given LAMMPS trajectories automatically.

Project description


arxiv:1910.12690 python version PyPI codecov Research Group

MDDatasetBuilder is a script to construct reference datasets for the training of neural network potentials from given LAMMPS trajectories.

Neural Network Based in Silico Simulation of Combustion Reactions, arXiv:1911.12252

Author: Jinzhe Zeng



Firstly, the latest version of Anaconda or Miniconda shoule be installed. And then use conda to install openbabel:

conda install openbabel -c conda-forge

Then install mddatasetbuilder can be installed with pip:

pip install git+

The installation process should be very quick, taking only a few minutes on a “normal” desktop computer.


Simple example

A LAMMPS dump file should be prepared. A LAMMPS bond file can be added for the addition information.

datasetbuilder -d dump.ch4 -b bonds.reaxc.ch4_new -a C H O -n ch4 -i 25

Here, dump.ch4 is the name of the dump file. bonds.reaxc.ch4_new is the name of the bond file, which is optional. C H O is the element in the trajectory. ch4 is the name of the dataset. 25 means the time step interval and the default value is 1.

Then you can generate Gaussian input files for each structure in the dataset and calculate the potential energy & atomic forces (assume the Gaussian 16 has already been installed.):

qmcalc -d dataset_ch4_GJf/000
qmcalc -d dataset_ch4_GJf/001

Next, prepare a DeePMD dataset and use DeePMD-kit to train a NN model.

preparedeepmd -p dataset_ch4_GJf -a C H O
cd train && dp train train.json

The runtime of the software depends on the amount of data. It is more suited to running on a server rather than desktop computer.


The MDDatasetBuilder package has been integrated into DP-GEN software.

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