Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Chen, An Wang, Zhilong Vidaurre, Karl Luigi Loza Han, Yanqiang Ye, Simin Tao, Kehao Wang, Shiwei Gao, Jing Li, Jinjin |
| author_facet | Chen, An Wang, Zhilong Vidaurre, Karl Luigi Loza Han, Yanqiang Ye, Simin Tao, Kehao Wang, Shiwei Gao, Jing Li, Jinjin |
| contents | Molecules and materials are the foundation for the development of modern advanced industries such as energy storage systems and semiconductor devices. However, traditional trial-and-error methods or theoretical calculations are highly resource-intensive, and extremely long R&D (Research and Development) periods cannot meet the urgent need for molecules/materials in industrial development. Machine learning (ML) methods based on big data are expected to break this dilemma. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of machine learning. The application of transfer learning lowers the data requirements for model training, which makes transfer learning stand out in researches addressing data quality issues. In this review, we summarize recent advances in transfer learning related to molecular and materials science. We focus on the application of transfer learning methods for the discovery of advanced molecules/materials, particularly, the construction of transfer learning frameworks for different systems, and how transfer learning can enhance the performance of models. In addition, the challenges of transfer learning are also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science Chen, An Wang, Zhilong Vidaurre, Karl Luigi Loza Han, Yanqiang Ye, Simin Tao, Kehao Wang, Shiwei Gao, Jing Li, Jinjin Materials Science Machine Learning Chemical Physics Molecules and materials are the foundation for the development of modern advanced industries such as energy storage systems and semiconductor devices. However, traditional trial-and-error methods or theoretical calculations are highly resource-intensive, and extremely long R&D (Research and Development) periods cannot meet the urgent need for molecules/materials in industrial development. Machine learning (ML) methods based on big data are expected to break this dilemma. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of machine learning. The application of transfer learning lowers the data requirements for model training, which makes transfer learning stand out in researches addressing data quality issues. In this review, we summarize recent advances in transfer learning related to molecular and materials science. We focus on the application of transfer learning methods for the discovery of advanced molecules/materials, particularly, the construction of transfer learning frameworks for different systems, and how transfer learning can enhance the performance of models. In addition, the challenges of transfer learning are also discussed. |
| title | Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2403.12982 |