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 | 4 | 21.05% |
| Property Prediction | 3 | 15.79% |
| Deep Learning | 2 | 10.53% |
| Graph Neural Network | 2 | 10.53% |
| BIG-bench Machine Learning | 2 | 10.53% |
| Benchmarking | 1 | 5.26% |
| Computational Efficiency | 1 | 5.26% |
| Molecular Property Prediction | 1 | 5.26% |
| Robust Design | 1 | 5.26% |
Shifted Softplus
