This is the essential feature of the early stage DeepMD, and it has been merged to the DeepMD now.
Basically we can train the network adaptively according to the current feedback. Namely, the scheme generates configurations that can't be predicted well and does DFT single point calculation on them. Therefore, it makes DeepMD more feasible.
Firstly, you can get a desired initial configuration from material project in POSCAR
format in the main directory of the package.
perp.py
will produce initial random files
python perp.py -h
usage: perp.py [-h] [-r] [-out OUTPUT] [-ra RATIO] [-div DIVIDES] [-si SIGMA]
[-den DENSITY]
[files [files ...]]
get perturbation from the exist POSCAR
positional arguments:
files list of POSCAR files (plain)
optional arguments:
-h, --help show this help message and exit
-r, --recursive scan recursively for OUTCAR files
-out OUTPUT, --output OUTPUT
Set the output iter parent directory of the POSCAR
-ra RATIO, --ratio RATIO
Set the configuration change ratio
-div DIVIDES, --divides DIVIDES
Set the divides of the density
-si SIGMA, --sigma SIGMA
Set the std_devi of the normal distribution
-den DENSITY, --density DENSITY
Set the center density of the whole phase graph
Specify the initial iteration path, which can be obtained from perp.py
"sys_dir": "/home/tgzhou/Research/DeepMD/Hydrogen/perp_data/iter"
Then input other parameters in the my_param.json
The entry of the package is param.py
, which reads the parameter from json file, for instance, my_param.json
.
python param.py my_param.json -c &> run.log
You can use python3 param.py -h
to check the optional argument.
python param.py -h
usage: param.py [-h] [-c] file
Deepmd ab-inito genrator
positional arguments:
file Get the json configuration file
optional arguments:
-h, --help show this help message and exit
-c, --continue_train continue from the generator_checkpoint.json
--continue_train
argument will restart the active training scheme.
After calculation, you will see the output directory.
iter iter_0 iter_1 ... iter_n
Each iter
directory has three subdirectories
deepmd lammps vasp
You can find a tensorflow graph model in deepmd
directory
ls deepmd/graph_0
checkpoint deepmd.json frozen_model.pb lcurve.out model.ckpt.data-00000-of-00001 model.ckpt.index model.ckpt.meta stat.avg.out stat.std.out