Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction

Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction

The performance prediction and rational design of energetic molecules (EMs) remain central challenges in their development. Traditional experimental methods are constrained by prohibitively high costs and inherent safety risks, highlighting the urgent requirement for efficient computational alternat...

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Bibliographic Details
Main Authors: Mingchi Gao, Tengxin Huang, Mingtian Li, Yingjun Zhang, Liangliang Wang, Junjie Ding
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Chemical Engineering Journal Advances
Subjects:
Energetic molecules
Quantitative structure–property relationship
Machine learning
Property prediction
Molecular design
Online Access:http://www.sciencedirect.com/science/article/pii/S2666821125001012
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http://www.sciencedirect.com/science/article/pii/S2666821125001012

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