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Models/SchNet

SchNet

Reported on 8 benchmarks across 2 tasks · 2 papers · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Miscellaneous5 results

Formation EnergyonMaterials Project
MAE· 2018-06-08
31.8
best: 17.47 (CartNet)
SOTA
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials arXiv:1806.03146
Formation EnergyonJARVIS-DFT
MAE· 2017-06-26
0.045
best: 0.02705 (CartNet)
SOTA
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions arXiv:1706.08566
Formation EnergyonOQM9HK
MAE· 2017-06-26
0.31
best: 0.03433 (CGNN Full Ensemble)
SOTA
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions arXiv:1706.08566
Formation EnergyonQM9
MAE· 2018-06-08
0.314
best: 0.09 (TensorNet)
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials arXiv:1806.03146
Formation EnergyonMaterials Project
MAE
35
best: 17.47 (CartNet)

Medical5 results

Atomistic DescriptiononMaterials Project
MAE· 2018-06-08
31.8
best: 17.47 (CartNet)
SOTA
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials arXiv:1806.03146
Atomistic DescriptiononJARVIS-DFT
MAE· 2017-06-26
0.045
best: 0.02705 (CartNet)
SOTA
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions arXiv:1706.08566
Atomistic DescriptiononOQM9HK
MAE· 2017-06-26
0.31
best: 0.03433 (CGNN Full Ensemble)
SOTA
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions arXiv:1706.08566
Atomistic DescriptiononQM9
MAE· 2018-06-08
0.314
best: 0.00467 (Uni-Mol)
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials arXiv:1806.03146
Atomistic DescriptiononMaterials Project
MAE
35
best: 17.47 (CartNet)