Formation Energy On Qm9

المقاييس

MAE

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

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

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