SchNet - A deep learning architecture for molecules and materials.

  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},
  volume={148 24},
  • 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
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