SchNet – A deep learning architecture for molecules and materials
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Abstract
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 that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.
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
- The Journal of Chemical Physics
- 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 TorontoNational Research Foundation of KoreaH2020 Marie Skłodowska-Curie ActionsBundesministerium für Bildung und ForschungNational Research FoundationDeutsche ForschungsgemeinschaftH2020 European Research CouncilMinistry of Science, ICT and Future PlanningEuropean Commission
- Keywords
- Deep learningChemical spaceComputer scienceArtificial intelligenceAb initioMolecular dynamicsArchitectureSpace (punctuation)QuantumPhysicsQuantum mechanicsBioinformaticsBiology
- Has abstract in OpenAlex
- yes