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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03704 |
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| _version_ | 1866915276106235904 |
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| author | Obuchi, Kiichi Yahagi, Yuta Toyama, Kiyohiko Tanaka, Shukichi Matsui, Kota |
| author_facet | Obuchi, Kiichi Yahagi, Yuta Toyama, Kiyohiko Tanaka, Shukichi Matsui, Kota |
| contents | In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. However, in conventional approaches, prediction models were often constructed using each type of data separately. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by graph convolutional neural networks (GCN) with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets. We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-modal cascade feature transfer for polymer property prediction Obuchi, Kiichi Yahagi, Yuta Toyama, Kiyohiko Tanaka, Shukichi Matsui, Kota Machine Learning In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. However, in conventional approaches, prediction models were often constructed using each type of data separately. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by graph convolutional neural networks (GCN) with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets. We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature. |
| title | Multi-modal cascade feature transfer for polymer property prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.03704 |