Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale...

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Bibliographic Details
Main Authors: Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard, Massimiliano Lupo Pasini
Format: Article
Language:English
Published: BMC 2022-10-01
Series:Journal of Cheminformatics
Subjects:
Graph neural networks
Distributed data parallelism
Surrogate models
Atomic modeling
Molecular dynamics
HOMO-LUMO gap
Online Access:https://doi.org/10.1186/s13321-022-00652-1

Internet

https://doi.org/10.1186/s13321-022-00652-1

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