Reference implementation of the GDML and sGDML force field models.
Project description
Symmetric Gradient Domain Machine Learning (sGDML)
Requirements:
- Python 2.7
- NumPy (>=1.13.0)
- SciPy
Getting started
Clone the repository
git clone https://github.com/stefanch/sGDML.git
cd sGDML
...or update your local copy
git pull origin master
Install
pip install -e .
Reconstruct your first force field
sgdml-get dataset ethanol
sgdml all ethanol.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
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