SchNet - A deep learning architecture for molecules and materials.

@article{Schtt2018SchNetA,
  title={SchNet - A deep learning architecture for molecules and materials.},
  author={Kristof T. Sch{\"u}tt and H. E. Sauceda and P.-J. Kindermans and A. Tkatchenko and K. M{\"u}ller},
  journal={The Journal of chemical physics},
  year={2018},
  volume={148 24},
  pages={
          241722
        }
}
  • Kristof T. Schütt, H. E. Sauceda, +2 authors K. Müller
  • Published 2018
  • Computer Science, Medicine, Physics, Chemistry
  • The Journal of chemical physics
  • Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet… CONTINUE READING
    310 Citations

    Figures, Tables, and Topics from this paper

    3DMolNet: A Generative Network for Molecular Structures
    • 3
    • Highly Influenced
    • PDF
    Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics
    • 19
    • PDF
    Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
    • 24
    • PDF
    Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
    • 22
    • PDF

    References

    SHOWING 1-10 OF 42 REFERENCES
    Quantum-chemical insights from deep tensor neural networks
    • 514
    • PDF
    Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
    • 230
    • PDF
    Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
    • 195
    • PDF
    Bypassing the Kohn-Sham equations with machine learning
    • 268
    • PDF
    Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
    • 322
    • PDF