Johannes Gasteiger, Janek Groß, Stephan Günnemann
We lifted 10 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| akirasosa/pytorch-dimenet | pwc_unofficial | 7 of 7 |
| klicperajo/dimenet | alias | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| Jn | Ran | akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py code served (permissive licence) · get_code("3275ba422c2a789a") |
| Jn_zeros | Ran | akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py code served (permissive licence) · get_code("ab2bb0b3e5be568c") |
| OutputBlock | Ran | klicperajo/dimenet/dimenet/model/dimenet.py pointer only (licence: NOASSERTION) · get_code("09f7b0a12a0269c6") |
| calculate_interatomic_distances | Ran | akirasosa/pytorch-dimenet/src/dimenet/functional.py code served (permissive licence) · get_code("7e6b3b45707a5703") |
| calculate_neighbor_angles | Ran | akirasosa/pytorch-dimenet/src/dimenet/functional.py code served (permissive licence) · get_code("c72e079ce4739527") |
| get_loader | Ran | akirasosa/pytorch-dimenet/src/dimenet/loader.py code served (permissive licence) · get_code("13510a6058a2e6e3") |
| spherical_bessel_formulas | Ran | akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py code served (permissive licence) · get_code("dad5a152d86c0b92") |
| to_tensor | Ran | akirasosa/pytorch-dimenet/src/dimenet/loader.py code served (permissive licence) · get_code("eb199682fba95610") |
| DimeNet | Not yet run | klicperajo/dimenet/dimenet/model/dimenet.py pointer only (licence: NOASSERTION) · get_code("0afbb5155226e133") |
| InteractionBlock | Not yet run | klicperajo/dimenet/dimenet/model/dimenet.py pointer only (licence: NOASSERTION) · get_code("d967dc5d7d0156c3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional information playing a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions and spherical harmonics to construct theoretically well-founded, orthogonal representations that achieve better performance than the currently prevalent Gaussian radial basis representations while using fewer than 1 /4 of the parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 76 % on MD17 and by 31 % on QM9. Our implementation is available online. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.03123")
get_code_for_paper("2003.03123")
have("2003.03123")
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