Graph Models
Schrödinger Network
Introduced by Schütt et al. in SchNet: A continuous-filter convolutional neural network for modeling quantum interactionsSchNet is an end-to-end deep neural network architecture based on continuous-filter convolutions. It follows the deep tensor neural network framework, i.e. atom-wise representations are constructed by starting from embedding vectors that characterize the atom type before introducing the configuration of the system by a series of interaction blocks.
Source: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Formation Energy | 3 | 27.27% |
| Property Prediction | 2 | 18.18% |
| BIG-bench Machine Learning | 2 | 18.18% |
| 3D Pose Estimation | 1 | 9.09% |
| Drug Discovery | 1 | 9.09% |
| Total Energy | 1 | 9.09% |
| Robust Design | 1 | 9.09% |
Shifted Softplus
