Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning
In this work, a high-throughput computation (HTC) and machine learning (ML) combined method was applied to identify the determining factors of the detonation velocity (vd) and detonation pressure (pd) of energetic molecules and screen potential high-energy molecules with acceptable stability in a hi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2024-12-01
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| Series: | Energetic Materials Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666647223000192 |
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| author | Wen Qian Jing Huang Shi-tai Guo Bo-wen Duan Wei-yu Xie Jian Liu Chao-yang Zhang |
| author_facet | Wen Qian Jing Huang Shi-tai Guo Bo-wen Duan Wei-yu Xie Jian Liu Chao-yang Zhang |
| author_sort | Wen Qian |
| collection | DOAJ |
| description | In this work, a high-throughput computation (HTC) and machine learning (ML) combined method was applied to identify the determining factors of the detonation velocity (vd) and detonation pressure (pd) of energetic molecules and screen potential high-energy molecules with acceptable stability in a high-throughput way. The HTC was performed based on 1725 sample molecules abstracted from a dataset of over 106 linear nitroaliphatics with 1- to 6-membered C backbones and three types of substituents, namely single nitro group (-NO2), nitroamine (-NNO2), and nitrate ester (-ONO2). ML models were established based on the HTC results to screen high-energy molecules and to identify the determining factors of vd and pd. Compared with quantum chemistry calculation results, the absolute relative errors of vd and pd obtained using the ML models were less than 3.63% and 5%, respectively. Furthermore, eight molecules with high energy and acceptable stability were selected as potential candidates. This study shows the high efficiency of the combination of HTC and ML in high-throughput screening. |
| format | Article |
| id | doaj-art-00d0ae36c582443fad66f199b63b26ff |
| institution | Kabale University |
| issn | 2666-6472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Energetic Materials Frontiers |
| spelling | doaj-art-00d0ae36c582443fad66f199b63b26ff2025-01-21T04:13:20ZengKeAi Communications Co. Ltd.Energetic Materials Frontiers2666-64722024-12-0154283292Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learningWen Qian0Jing Huang1Shi-tai Guo2Bo-wen Duan3Wei-yu Xie4Jian Liu5Chao-yang Zhang6Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Computer Applications, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, China; Beijing Computational Science Research Center, Beijing, 100048, China; Corresponding author. Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621999, China.In this work, a high-throughput computation (HTC) and machine learning (ML) combined method was applied to identify the determining factors of the detonation velocity (vd) and detonation pressure (pd) of energetic molecules and screen potential high-energy molecules with acceptable stability in a high-throughput way. The HTC was performed based on 1725 sample molecules abstracted from a dataset of over 106 linear nitroaliphatics with 1- to 6-membered C backbones and three types of substituents, namely single nitro group (-NO2), nitroamine (-NNO2), and nitrate ester (-ONO2). ML models were established based on the HTC results to screen high-energy molecules and to identify the determining factors of vd and pd. Compared with quantum chemistry calculation results, the absolute relative errors of vd and pd obtained using the ML models were less than 3.63% and 5%, respectively. Furthermore, eight molecules with high energy and acceptable stability were selected as potential candidates. This study shows the high efficiency of the combination of HTC and ML in high-throughput screening.http://www.sciencedirect.com/science/article/pii/S2666647223000192Energetic moleculeMolecular designHigh-throughput computationMachine learningDetonation property |
| spellingShingle | Wen Qian Jing Huang Shi-tai Guo Bo-wen Duan Wei-yu Xie Jian Liu Chao-yang Zhang Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning Energetic Materials Frontiers Energetic molecule Molecular design High-throughput computation Machine learning Detonation property |
| title | Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning |
| title_full | Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning |
| title_fullStr | Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning |
| title_full_unstemmed | Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning |
| title_short | Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning |
| title_sort | identifying the determining factors of detonation properties for linear nitroaliphatics with high throughput computation and machine learning |
| topic | Energetic molecule Molecular design High-throughput computation Machine learning Detonation property |
| url | http://www.sciencedirect.com/science/article/pii/S2666647223000192 |
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