Prediction Model of Material Properties Based on Feature Fusion and Convolutional Neural Network
Aiming at the problem that most machine learning models need a lot of prior knowledge and manual selection of feature vectors in the prediction of material properties, a convolutional neural network model OPCNN (Orbital of Electron and Periodic table CNN) is established by feature fusion based on tw...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2338 |
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| Summary: | Aiming at the problem that most machine learning models need a lot of prior knowledge and manual selection of feature vectors in the prediction of material properties, a convolutional neural network model OPCNN (Orbital of Electron and Periodic table CNN) is established by feature fusion based on two descriptors, electronic orbit matrix and periodic table method. The experimental data show that compared with other prediction models, OPCNN has better performance on the bandgap, heat of formation and formation energy datasets, with the mean absolute error of 0. 26 eV, 0. 037 KJ/mol and 0. 073 eV/atom, respectively, and the R2 is more than 91% . OPCNN has lower requirements for prior knowledge while ensuring the accuracy of prediction. It only needs the information in the periodic table to predict the material properties. The idea of feature fusion can make feature design more flexible, which is conducive to the rapid and accurate prediction of new material systems. |
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| ISSN: | 1007-2683 |