Reference implementation of the GDML and sGDML force field models.
Project description
Symmetric Gradient Domain Machine Learning (sGDML)
For more details visit: http://sgdml.org/
Documentation can be found here: http://sgdml.org/doc/
Requirements:
- Python 2.7/3.7
- NumPy (>=1.13.0)
- SciPy
Getting started
Stable release
Most systems come with the default package manager for Python pip
already preinstalled. We 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
Clone the repository
git clone https://github.com/stefanch/sGDML.git
cd sGDML
...or update your existing local copy with
git pull origin master
Install
pip install -e .
Using the flag --user
, we 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.
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('examples/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)
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. arXiv:1812.04986
-
[4] Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., & Tkatchenko, A., Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces arXiv:1901.06594
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