Extended atom-based and bond-based group contribution descriptor and its application to melting point prediction of energetic compounds-Reference-Cited by-同舟云学术

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Extended atom-based and bond-based group contribution descriptor and its application to melting point prediction of energetic compounds

Published:2023-12 Issue: Volume:243 Page:105021
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Kong Dingling,Luan Yue,Zhao Xiaowei,Lu Yanhua,Li Wei,Zhang Qingyou^ORCID,Pang Aimin

Funder

Open Research Fund Program of Science and Technology on Aerospace Chemical Power Laboratory

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference35 articles.

1. Density prediction models for energetic compounds merely using molecular topology;Yang;J. Chem. Inf. Model.,2021

2. Multicomponent metal-organic frameworks with aggregation-induced emission characteristics as fluorescence sensor array for the identification of energetic compounds;Zhu;Sensor. Actuator. B Chem.,2021

3. A simple method to predict melting points of non-aromatic energetic compounds;Alamdari;Fluid Phase Equil.,2010

4. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization;Nigsch;J. Chem. Inf. Model.,2006

5. QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network;Wang;Chemometr. Intell. Lab. Syst.,2015

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A bayesian deep learning-based approach to group contribution methods: A study on cannabinoids and terpenes;Fluid Phase Equilibria;2024-08

2. Advancements in methodologies and techniques for the synthesis of energetic materials: A review;Energetic Materials Frontiers;2024-06

3. The prediction of crystal densities of a big data set using 1D and 2D structure features;Structural Chemistry;2024-02-06

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