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Formation Energy On Qm9

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MAE

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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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Formation Energy On Qm9 | SOTA | HyperAI