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Formation Energy
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On the QM9 dataset the numbers reported in the table are the mean absolute error in eV on the target variable U0 divided by U0's chemical accuracy, which is equal to 0.043.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Lin-Group-at-UMass/FBGNN-MBE
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning
https://github.com/Microsoft/gated-graph-neural-network-samples
https://github.com/Samyu0304/thought-propagation
https://github.com/Saro00/DGN
https://github.com/Tony-Y/cgnn
https://github.com/akirasosa/pytorch-dimenet
https://github.com/cts198859/deeprl_dist
https://github.com/cts198859/deeprl_network
https://github.com/divelab/AIRS
Periodic Graph Transformers for Crystal Material Property Prediction
https://github.com/dmlc/dgl/tree/master/examples/pytorch/dimenet
https://github.com/dongchen06/macacc
https://github.com/fredjo89/heterogeneous-mpnn
https://github.com/gajeelSF/Reinforcement-Learning-Branch-and-Bound
https://github.com/intellabs/matsciml
https://github.com/jla-gardner/graph-pes
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
https://github.com/jmg764/Molecular-Scalar-Coupling-Constant-Prediction-using-SchNet
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
https://github.com/jmg764/Predicting-Molecular-Scalar-Couplings-using-SchNet
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
https://github.com/lukecavabarrett/pna
https://github.com/materialsvirtuallab/matgl
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
https://github.com/nrel/m2p
https://github.com/priba/nmp_qc
https://github.com/priba/siamese_ged
https://github.com/smiles724/protmd
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
https://github.com/teddykoker/mpnn-for-quantum-chem
https://github.com/tisabe/jraph_mpeu
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/tomdbar/eco-dqn
https://github.com/toshi-k/kaggle-champs-scalar-coupling
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/toshi-k/kaggle-trends-assessment-prediction
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/xiuyu0000/new_papers_codes/tree/main/Cybertron
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.