SchNet: a continuous-filter convolutional neural network for modeling quantum interactions. Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Fan, Huiling: The digital asset value and currency supervision under deep learning and blockchain technology (2022)
  2. Huang, Daniel; Teng, Chong; Bao, Junwei Lucas; Tristan, Jean-Baptiste: mad-GP: automatic differentiation of Gaussian processes for molecules and materials (2022)
  3. James Gardner, Oscar A. Douglas-Gallardo, Wojciech G. Stark, Julia Westermayr, Svenja M. Janke, Scott Habershon, Reinhard J. Maurer: NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase (2022) arXiv
  4. Domingo, L.; Borondo, F.: Deep learning methods for the computation of vibrational wavefunctions (2021)
  5. Geist, Moritz; Petersen, Philipp; Raslan, Mones; Schneider, Reinhold; Kutyniok, Gitta: Numerical solution of the parametric diffusion equation by deep neural networks (2021)
  6. Schoenholz, Samuel S.; Cubuk, Ekin D.: JAX, M.D. a framework for differentiable physics (2021)
  7. Zepeda-Núñez, Leonardo; Chen, Yixiao; Zhang, Jiefu; Jia, Weile; Zhang, Linfeng; Lin, Lin: Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks (2021)
  8. Han, Jiequn; Zhang, Linfeng; E, Weinan: Solving many-electron Schrödinger equation using deep neural networks (2019)
  9. Kriege, Nils M.; Neumann, Marion; Morris, Christopher; Kersting, Kristian; Mutzel, Petra: A unifying view of explicit and implicit feature maps of graph kernels (2019)