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Physical Pooling Functions in Graph Neural Networks for Molecular
  Property Prediction

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction

27 July 2022
Artur M. Schweidtmann
Jan G. Rittig
Jana M. Weber
Martin Grohe
Manuel Dahmen
K. Leonhard
Alexander Mitsos
ArXivPDFHTML

Papers citing "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction"

3 / 3 papers shown
Title
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
Liu Junchi
Tang Ying
Tretiak Sergei
Duan Wenhui
Zhou Liujiang
33
0
0
01 May 2025
Generative AI and Process Systems Engineering: The Next Frontier
Generative AI and Process Systems Engineering: The Next Frontier
Benjamin Decardi-Nelson
Abdulelah S. Alshehri
Akshay Ajagekar
Fengqi You
AI4CE
LLMAG
19
23
0
15 Feb 2024
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
229
1,935
0
09 Jun 2018
1