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Formation Energy On Qm9

Métriques

MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAE	Paper Title	Repository
PhysNet	0.19	PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
TensorNet	0.09	TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials	-
SchNet	0.314	Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
HIP-NN	0.256	Hierarchical modeling of molecular energies using a deep neural network	-
Wigner Kernels	0.100 ± 0.003	Wigner kernels: body-ordered equivariant machine learning without a basis	-
PAMNet	0.136	A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
MXMNet	0.137	Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
MEGNet-Full	0.21	Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
HMGNN	0.138	Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
ALIGNN	0.30	Atomistic Line Graph Neural Network for Improved Materials Property Predictions
DimeNet	0.185	Directional Message Passing for Molecular Graphs
SchNet-edge-update	0.242	Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
MPNN	0.49	Neural Message Passing for Quantum Chemistry
HDAD+KRR	0.58	Machine learning prediction errors better than DFT accuracy	-
DeepMoleNet	0.141	Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning	-
MEGNet-simple	0.28	Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
PhysNet-ens5	0.14	PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
xGPR -- Gaussian process, graph convolution kernel	0.167	Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

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