mlsanityData to 14 Oct 2026

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

arXiv 1706.08566 ↗First posted 26 Jun 2017stat.ML physics.chem-phcited by 571

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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 571 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jun 2017.

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Citing papers per year: 2021: 76; 2022: 96; 2023: 109; 2024: 97; 2025: 104; 2026: 89.

571 papers.

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  38. 2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58

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