cationly / sGDML

Reference implementation of the Symmetric Gradient Domain Machine Learning model

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.py 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, Igor, 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., Müller, K.-R., & Tkatchenko, A., Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields. arXiv preprint, 1802.09238 (2018)
  arXiv:1802.09238