A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions
Understanding the astronomical evolution of celestial regions necessitates reconstructing evolutionary pathways within dynamic physical environments, which heavily relies on precise and comprehensive astrochemical reaction networks. Traditional methods rely on expert knowledge and incur substantial...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0118 |
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| _version_ | 1850276561473765376 |
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| author | Jiawei Wang Yanan Zhang Haili Bu Yang Lu Manni Duan Donghui Quan Peng Qiu |
| author_facet | Jiawei Wang Yanan Zhang Haili Bu Yang Lu Manni Duan Donghui Quan Peng Qiu |
| author_sort | Jiawei Wang |
| collection | DOAJ |
| description | Understanding the astronomical evolution of celestial regions necessitates reconstructing evolutionary pathways within dynamic physical environments, which heavily relies on precise and comprehensive astrochemical reaction networks. Traditional methods rely on expert knowledge and incur substantial time and cost. In this study, we introduce a novel 2-stage end-to-end deep learning approach for predicting astrochemical reaction products, marking the first application of these techniques in this field. Our method comprises 2 primary phases: a generative phase leveraging a graph encoder and transformer architecture for the generation of potential reaction products, and a contrastive learning-based phase for re-ranking the potential products. We rigorously evaluated the performance of our approach using the ChemiVerse dataset. Experimental results show notable accuracy rates of 82.4% (Top-1), 91.4% (Top-3), 93.0% (Top-5), and 93.7% (Top-10). This study demonstrates the feasibility and effectiveness of using advanced deep learning techniques for end-to-end astrochemical reaction prediction. |
| format | Article |
| id | doaj-art-26ab136c48cd402f94e199eb505aa414 |
| institution | OA Journals |
| issn | 2771-5892 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Intelligent Computing |
| spelling | doaj-art-26ab136c48cd402f94e199eb505aa4142025-08-20T01:50:13ZengAmerican Association for the Advancement of Science (AAAS)Intelligent Computing2771-58922025-01-01410.34133/icomputing.0118A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical ReactionsJiawei Wang0Yanan Zhang1Haili Bu2Yang Lu3Manni Duan4Donghui Quan5Peng Qiu6Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Zhejiang Lab, Hangzhou, Zhejiang, China.Understanding the astronomical evolution of celestial regions necessitates reconstructing evolutionary pathways within dynamic physical environments, which heavily relies on precise and comprehensive astrochemical reaction networks. Traditional methods rely on expert knowledge and incur substantial time and cost. In this study, we introduce a novel 2-stage end-to-end deep learning approach for predicting astrochemical reaction products, marking the first application of these techniques in this field. Our method comprises 2 primary phases: a generative phase leveraging a graph encoder and transformer architecture for the generation of potential reaction products, and a contrastive learning-based phase for re-ranking the potential products. We rigorously evaluated the performance of our approach using the ChemiVerse dataset. Experimental results show notable accuracy rates of 82.4% (Top-1), 91.4% (Top-3), 93.0% (Top-5), and 93.7% (Top-10). This study demonstrates the feasibility and effectiveness of using advanced deep learning techniques for end-to-end astrochemical reaction prediction.https://spj.science.org/doi/10.34133/icomputing.0118 |
| spellingShingle | Jiawei Wang Yanan Zhang Haili Bu Yang Lu Manni Duan Donghui Quan Peng Qiu A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions Intelligent Computing |
| title | A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions |
| title_full | A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions |
| title_fullStr | A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions |
| title_full_unstemmed | A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions |
| title_short | A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions |
| title_sort | two stage end to end deep learning approach for predicting astrochemical reactions |
| url | https://spj.science.org/doi/10.34133/icomputing.0118 |
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