Important: This package will not be further developed and supported. Please consider switching to our new pytorch-based package SchNetPack!
SchNet is a deep learning architecture that allows for spatially and chemically resolved insights into quantum-mechanical observables of atomistic systems.
Requirements:
- python 3.4
- ASE
- numpy
- tensorflow (>=1.0)
See the scripts folder for training and evaluation of SchNet
model for predicting the total energy (U0) for the GDB-9 data set.
python3 setup.py install
Download and convert QM9 data set:
python3 load_qm9.py <qm9destination>
Train QM9 energy (U0) prediction:
python3 train_energy_force.py <qm9destination>/qm9.db ./modeldir ./split50k.npz
--ntrain 50000 --nval 10000 --fit_energy --atomref <qm9destination>/atomref.npz
Predict force and energy for fullerene C20 configuration
python scripts/example_md_predictor.py ./models/c20/ ./models/c20/C20.xyz
Relax geometry:
python scripts/example_md_predictor.py ./models/c20/ ./models/c20/C20.xyz --relax
If you use SchNet in your research, please cite:
K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.
Advances in Neural Information Processing Systems 30, pp. 992-1002 (2017)
K.T. Schütt. F. Arbabzadah. S. Chmiela, K.-R. Müller, A. Tkatchenko.
Quantum-chemical insights from deep tensor neural networks.
Nature Communications 8. 13890 (2017)
doi: 10.1038/ncomms13890