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All Projects → atomistic-machine-learning → dtnn

atomistic-machine-learning / dtnn

Licence: MIT license
Deep Tensor Neural Network

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machine-learningquantum-chemistry

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Deep Tensor Neural Networks

The deep tensor neural network (DTNN) enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems.

Requirements:

  • python 3.4
  • ASE
  • numpy
  • tensorflow (>=1.0)

See the examples folder for scripts for training and evaluation of a DTNN model for predicting the total energy (U0) for the GDB-9 data set. The data set will be downloaded and converted automatically.

Basic usage:

python train_dtnn_gdb9.py -h

If you use deep tensor neural networks in your research, please cite:

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

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