Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets

Yao-Yuan Huo; Jun Jiang

doi:10.1063/1674-0068/cjcp2208128

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Yao-Yuan Huo, Jun Jiang. Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets[J]. Chinese Journal of Chemical Physics . doi: 10.1063/1674-0068/cjcp2208128

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Yao-Yuan Huo, Jun Jiang. Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets[J]. Chinese Journal of Chemical Physics . doi: 10.1063/1674-0068/cjcp2208128

Yao-Yuan Huo, Jun Jiang. Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets[J]. Chinese Journal of Chemical Physics . doi: 10.1063/1674-0068/cjcp2208128

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Transferring Graph Neural Network Models for Predicting Bond Dissociation Energy between Datasets

doi: 10.1063/1674-0068/cjcp2208128

Yao-Yuan Huo,
Jun Jiang^,

Hefei National Laboratory for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China

More Information

Corresponding author: E-mail: jiangj1@ustc.edu.cn
Received Date: 2022-08-25
Accepted Date: 2023-04-22

Available Online: 2023-04-24

Abstract

Abstract

Machine learning (ML) approaches like neural networks have been widely used in chemical researches for fast estimating chemical properties. Generating ML models of good precision requires datasets of high quality, which can be difficult to obtain. In this work, we trained graph neural network (GNN) models from different datasets and verified transferring of the models to other datasets. Our result shows that cross-dataset evaluation can give less accurate but still correlative prediction results on different datasets. Errors are mainly due to systematic errors. The value range of prediction result is highly related to the range of training set. The precisions of different bonds show different distributions. C–H bond constantly gets the highest precision in the tested bonds.
- Machine learning,
- Graph neural network,
- Cross validation

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References(20)

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