Machine Learning Enabled Quickly Predicting of Detonation Properties of N‐Containing Molecules for Discovering New Energetic Materials-Reference-Cited by-同舟云学术

Machine Learning Enabled Quickly Predicting of Detonation Properties of N‐Containing Molecules for Discovering New Energetic Materials

Published:2021-04-19 Issue:6 Volume:4 Page:2100057
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Hou Fang¹,Ma Yi²,Hu Zheng¹,Ding Shining¹,Fu Haihan¹,Wang Li¹³,Zhang Xiangwen¹³,Li Guozhu¹³^ORCID

Affiliation:

1. Key Laboratory for Green Chemical Technology of Ministry of Education School of Chemical Engineering and Technology Tianjin University Tianjin 300072 China

2. College of Intelligence and Computing Tianjin University Tianjin 300072 China

3. Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Multidisciplinary,Modelling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202100057

Reference33 articles.

1. Computational study about the derivatives of pyrrole as high-energy-density compounds

2. On some strategies to design new high energy density molecules

3. Novel Nitrogen‐Rich Tetrazine‐Based High Energy Density Molecules: Molecular Design and Computational Studies

4. Computational studies on the infrared vibrational spectra, thermodynamic properties, detonation properties, and pyrolysis mechanism of octanitrocubane

5. Theoretical studies on the structures, heats of formation, energetic properties and pyrolysis mechanisms of nitrogen-rich difurazano[3,4-b:3′,4′-e]piperazine derivatives and their analogues

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