Project AURIONAI Use-Cases Research & Innovation
Technische Universitat Berlin
scientificfamily

SchNet

Continuous-filter convolutional network for modelling quantum interactions in molecules, such as energies and forces. Paper by Schutt et al. (2017).

Technische Universitat BerlinTechnische Universitat BerlinGermany
Figure from the SchNet paper: comparison of discrete and continuous filters for modelling atom positions.
Levelfamily
Classscientific
Parametersnot in the notes
Contextnot in the notes
Released26 Jun 2017
Opennessopen source
Runs onnot in the notes
Statusactive

R&D Topics

Equivariant graph networks for moleculesexemplar

Identity

Developer
Technische Universitat Berlin
Family
not in the notes
Released
26 Jun 2017 sourcesannouncement page
Weights published
–
Record checked
12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
GNN Continuous-filter convolutions over atom positions
Total parameters
– sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
molecule
Output
molecule
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
open source
Licence
MIT License sources
Access routes
not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
Licence is that of the PyTorch Geometric implementation (MIT).
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. arxiv.org/abs/1706.08566 https://arxiv.org/abs/1706.08566
  2. GitHub - pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch github.com https://github.com/pyg-team/pytorch_geometric
  3. pytorch-geometric.readthedocs.io/en/latest/modules/nn.html https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html
  4. huggingface.co/datasets/pwc-archive/methods https://huggingface.co/datasets/pwc-archive/methods
  5. arxiv.org/html/1706.08566 https://arxiv.org/html/1706.08566

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "SchNet", https://aurion.semai.id/models/schnet/, records of October 2026.