SchNet – A deep learning architecture for molecules and materials
Open accessAbstract
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.
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An ensemble of six SchNet NNPs was trained on the final SCAN+D3(BJ) database, with six independent training runs using different randomly split parts of the DFT dataset (approximately 80% training, 10% validation, 10% test).
“An ensemble of six SchNet51 NNPs was trained on the final SCAN+D3(BJ) database. The six independent training runs used different, randomly split parts of the DFT dataset with approximately 80% of the data points as training set and 10% as validation and test set, respectively.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [51]
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- Authors
- Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R.
- Affiliations
- Technische Universität Berlin (DE)
- Fritz Haber Institute of the Max Planck Society (DE)
- University of Luxembourg (LU)
- Korea University (KR)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2018-03-29
- Volume
- 148
- Issue
- 24
- Pages
- 241722-241722
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2778051509
- Cited by
- 2,217
- References
- 57
- Citations by year
- 2026: 287 · 2025: 362 · 2024: 345 · 2023: 337 · 2022: 272 · 2021: 263 · 2020: 213 · 2019: 108 · 2018: 27 · 2012: 1
- FWCI
- 57.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion)
- arXiv (Cornell University)
- Bulletin of the American Physical Society
- Funders
- Deutsche Forschungsgemeinschaft (MU 987/20-1)
- National Research Foundation of Korea (2012-005741)
- Ministry of Science, ICT and Future Planning (2017-0-00451)
- Bundesministerium für Bildung und Forschung (01IS14013A)
- H2020 European Research Council (BeStMo)
- H2020 Marie Skłodowska-Curie Actions (657679)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- SchNet, graph neural networks, atomistic systems, potential energy surface, molecular dynamics, C20 fullerene, quantum mechanical properties, materials, molecules
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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