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
    View on PubMed
    orbilu.uni.lu
    310 Citations
    Highly Influential Citations
    15
    Background Citations
    53
    Methods Citations
    39
    Results Citations
    2

    Figures, Tables, and Topics from this paper

    Figures and Tables

    Explore Further: Topics Discussed in This Paper

    310 Citations
    Citation Type

    Citation Type

    Learning representations of atomistic systems with deep neural networks
    • 1
    • PDF
    3DMolNet: A Generative Network for Molecular Structures
    • 3
    • Highly Influenced
    • PDF
    Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
    • 79
    • PDF
    Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics
    • 19
    • PDF
    Quantum machine learning using atom-in-molecule-based fragments selected on the fly
    • 8
    • PDF
    Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
    • 24
    • PDF
    A deep neural network for molecular wave functions in quasi-atomic minimal basis representation.
    • 6
    • PDF
    Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
    • 22
    • PDF
    Deep integration of machine learning into computational chemistry and materials science
    • PDF
    Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space.
    • 3
    • PDF

    References

    SHOWING 1-10 OF 42 REFERENCES
    Quantum-chemical insights from deep tensor neural networks
    • 514
    • PDF
    Machine Learning of Molecular Electronic Properties in Chemical Compound Space
    • 320
    • PDF
    Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
    • 302
    • 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
    Development of a machine learning potential for graphene
    • 61
    • PDF
    Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
    • 322
    • PDF
    How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
    • 219
    • PDF
    Machine learning of accurate energy-conserving molecular force fields
    • 325
    • PDF