Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives

Tian, XL; Song, SW; Chen, F; Qi, XJ; Wang, Y; Zhang, QH

Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives

Tian, XL Song, SW Chen, F

Qi, XJ Wang, Y Zhang, QH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energetic Materials Frontiers, 2022, 3, (3), pp. 177-186
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Tian, XL
dc.contributor.author	Song, SW
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Qi, XJ
dc.contributor.author	Wang, Y
dc.contributor.author	Zhang, QH
dc.date.accessioned	2023-03-28T02:49:56Z
dc.date.available	2023-03-28T02:49:56Z
dc.date.issued	2022-09-01
dc.identifier.citation	Energetic Materials Frontiers, 2022, 3, (3), pp. 177-186
dc.identifier.issn	2666-6472
dc.identifier.issn	2666-6472
dc.identifier.uri	http://hdl.handle.net/10453/168661
dc.description.abstract	Predicting chemical properties is one of the most important applications of machine learning. In recent years, the prediction of the properties of energetic materials using machine learning has been receiving more attention. This review summarized recent advances in predicting energetic compounds’ properties (e.g., density, detonation velocity, enthalpy of formation, sensitivity, the heat of the explosion, and decomposition temperature) using machine learning. Moreover, it presented general steps for applying machine learning to the prediction of practical chemical properties from the aspects of data, molecular representation, algorithms, and general accuracy. Additionally, it raised some controversies specific to machine learning in energetic materials and its possible development directions. Machine learning is expected to become a new power for driving the development of energetic materials soon.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Energetic Materials Frontiers
dc.relation.isbasedon	10.1016/j.enmf.2022.07.005
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-28T02:49:55Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	3

Abstract:

Predicting chemical properties is one of the most important applications of machine learning. In recent years, the prediction of the properties of energetic materials using machine learning has been receiving more attention. This review summarized recent advances in predicting energetic compounds’ properties (e.g., density, detonation velocity, enthalpy of formation, sensitivity, the heat of the explosion, and decomposition temperature) using machine learning. Moreover, it presented general steps for applying machine learning to the prediction of practical chemical properties from the aspects of data, molecular representation, algorithms, and general accuracy. Additionally, it raised some controversies specific to machine learning in energetic materials and its possible development directions. Machine learning is expected to become a new power for driving the development of energetic materials soon.

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http://hdl.handle.net/10453/168661