SchNet 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 interactionsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Formation Energy | 3 | 25.00% |
Property Prediction | 2 | 16.67% |
Graph Neural Network | 2 | 16.67% |
BIG-bench Machine Learning | 2 | 16.67% |
Robust Design | 1 | 8.33% |
3D Pose Estimation | 1 | 8.33% |
Drug Discovery | 1 | 8.33% |