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Formation Energy On Qm9

평가 지표

MAE

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
MAE
Paper TitleRepository
PhysNet0.19PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges-
TensorNet0.09TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials-
SchNet0.314Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials-
HIP-NN0.256Hierarchical modeling of molecular energies using a deep neural network-
Wigner Kernels0.100 ± 0.003Wigner kernels: body-ordered equivariant machine learning without a basis-
PAMNet0.136A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
MXMNet0.137Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures-
MEGNet-Full0.21Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals-
HMGNN0.138Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties-
ALIGNN0.30Atomistic Line Graph Neural Network for Improved Materials Property Predictions-
DimeNet0.185Directional Message Passing for Molecular Graphs-
SchNet-edge-update0.242Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials-
MPNN0.49Neural Message Passing for Quantum Chemistry-
HDAD+KRR0.58Machine learning prediction errors better than DFT accuracy-
DeepMoleNet0.141Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning-
MEGNet-simple0.28Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals-
PhysNet-ens50.14PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges-
xGPR -- Gaussian process, graph convolution kernel0.167Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability-
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Formation Energy On Qm9 | SOTA | HyperAI초신경