Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis

Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis

In this study, machine learning (ML)-assisted regression modeling was conducted to predict the thermal decomposition temperatures and explore the factors that correlate with the thermal stability of energetic materials (EMs). The modeling was performed based on a dataset consisting of 885 various co...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhi-xiang Zhang, Yi-lin Cao, Chao Chen, Lin-yuan Wen, Yi-ding Ma, Bo-zhou Wang, Ying-zhe Liu
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2024-12-01
Series:Energetic Materials Frontiers
Subjects:
Energetic compound
Molecular descriptor
Thermal decomposition temperature
Machine learning
Feature analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S266664722300043X
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S266664722300043X

Similar Items