MDDatasetBuilder is a script to construct reference datasets for the training of neural network potentials from given LAMMPS trajectories.
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation, Nature Communications, 11, 5713 (2020), DOI: 10.1038/s41467-020-19497-z
Author: Jinzhe Zeng
Email: [email protected]
MDDatasetBuilder can be installed with pip:
pip install mddatasetbuilder
The installation process should be very quick, taking only a few minutes on a “normal” desktop computer.
datasetbuilder -d dump.ch4 -b bonds.reaxc.ch4_new -a C H O -n ch4 -i 25
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 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.
dpgen init_reaction reaction.json machine.json
See DP-GEN documentation for details. Arguments of
reaction.json can be found here.
machine.json is described here, where
reaxff_command is the LAMMPS command (
build_command is the MDDatasetbuilder command (
fp_command is the Gaussian 16 command (
g16 < input || :).
The genereated data can be used to continue DP-GEN concurrent learning workflow. Read Energy & Fuels, 2021, 35 (1), 762–769 for details.