Symmetric Gradient Domain Machine Learning (sGDML)
For more details visit: sgdml.org
Documentation can be found here: docs.sgdml.org
Requirements:
- Python 3.7+
- PyTorch (>=1.8)
- NumPy (>=1.19)
- SciPy (>=1.1)
Optional:
- ASE (>=3.16.2) (to run atomistic simulations)
Getting started
Stable release
Most systems come with the default package manager for Python pip already preinstalled. Install sgdml by simply calling:
$ pip install sgdml
The sgdml command-line interface and the corresponding Python API can now be used from anywhere on the system.
Development version
(1) Clone the repository
$ git clone https://github.com/stefanch/sGDML.git
$ cd sGDML
...or update your existing local copy with
$ git pull origin master
(2) Install
$ pip install -e .
Using the flag --user, you can tell pip to install the package to the current users's home directory, instead of system-wide. This option might require you to update your system's PATH variable accordingly.
Optional dependencies
Some functionality of this package relies on third-party libraries that are not installed by default. These optional dependencies (or "package extras") are specified during installation using the "square bracket syntax":
$ pip install sgdml[<optional1>]
Atomic Simulation Environment (ASE)
If you are interested in interfacing with ASE to perform atomistic simulations (see here for examples), use the ase keyword:
$ pip install sgdml[ase]
Reconstruct your first force field
Download one of the example datasets:
$ sgdml-get dataset ethanol_dft
Train a force field model:
$ sgdml all ethanol_dft.npz 200 1000 5000
Query a force field
import numpy as np
from sgdml.predict import GDMLPredict
from sgdml.utils import io
r,_ = io.read_xyz('geometries/ethanol.xyz') # 9 atoms
print(r.shape) # (1,27)
model = np.load('models/ethanol.npz')
gdml = GDMLPredict(model)
e,f = gdml.predict(r)
print(e.shape) # (1,)
print(f.shape) # (1,27)Authors
- Stefan Chmiela
- Jan Hermann
We appreciate and welcome contributions and would like to thank the following people for participating in this project:
- Huziel Sauceda
- Igor Poltavsky
- Luis Gálvez
- Danny Panknin
- Grégory Fonseca
References
-
[1] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R., Machine Learning of Accurate Energy-conserving Molecular Force Fields. Science Advances, 3(5), e1603015 (2017)
10.1126/sciadv.1603015 -
[2] Chmiela, S., Sauceda, H. E., Müller, K.-R., & Tkatchenko, A., Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields. Nature Communications, 9(1), 3887 (2018)
10.1038/s41467-018-06169-2 -
[3] Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., & Tkatchenko, A., sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning. Computer Physics Communications, 240, 38-45 (2019) 10.1016/j.cpc.2019.02.007