Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach-Reference-Cited by-同舟云学术

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Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach

Published:2024 Issue:6 Volume:3 Page:1293-1306
ISSN:2753-1457
Container-title:Energy Advances
language:en
Short-container-title:Energy Adv.

Author:

Pandey Shubham Kumar¹,Roy Kunal¹^ORCID

Affiliation:

1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology Jadavpur University, Kolkata 700032, India

Abstract

ML-based q-RASPR provides better predictions than QSPR for the performance and stability parameters of energetic materials (EMs).

Funder

All India Council for Technical Education

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/YA/D4YA00215F

Reference57 articles.

1. J. P.Agrawal , High Energy Materials: Propellants, Explosives and Pyrotechnics , John Wiley & Sons , 2010

2. Design and Synthesis of Energetic Materials

3. A review of advanced high performance, insensitive and thermally stable energetic materials emerging for military and space applications

4. Simple correlation for predicting detonation velocity of ideal and non-ideal explosives

5. Prediction of Detonation Velocity and N−O Composition of High Energy C−H−N−O Explosives by Means of Artificial Neural Networks

Cited by 1 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. How to correctly develop q-RASAR models for predictive cheminformatics;Expert Opinion on Drug Discovery;2024-07-05

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