SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.143 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- arXiv (Cornell University)
- Topic
- Machine Learning in Materials Science
- Field
- Materials Science
- Canadian institutions
- —
- Funders
- Institute for Information and Communications Technology PromotionBanting and Best Diabetes Centre, University of TorontoBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftNational Research FoundationEuropean Commission
- Keywords
- Computer sciencePotential energy surfaceQuantumGridConvolutional neural networkDiscretizationDeep learningChemical spaceFilter (signal processing)Invariant (physics)Benchmark (surveying)Quantum dynamicsDifferentiable functionArtificial intelligenceTheoretical computer scienceStatistical physicsMoleculePhysicsQuantum mechanicsChemistryMathematicsComputer visionGeometry
- Has abstract in OpenAlex
- yes