Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning-Reference-Cited by-同舟云学术

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Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning

Published:2024-09 Issue: Volume: Page:
ISSN:2666-6472
Container-title:Energetic Materials Frontiers
language:en
Short-container-title:Energetic Materials Frontiers

Author:

Chen Peng,Liu Hai-tao,Zhang Chao-yang,Huang Xin

Publisher

Elsevier BV

Reference48 articles.

1. A promising high-energy-density material;Zhang;Nat Commun,2017

2. Sensitivity and Performance of Energetic Materials;Jungová;Propellants, Explos Pyrotech.,2016

3. A series of energetic metal pentazolate hydrates;Xu;Nature,2017

4. Tri-explosophoric groups driven fused energetic heterocycles featuring superior energetic and safety performances outperforms HMX;Li;Nat Commun,2022

5. The effect of descriptor choice in machine learning models for ionic liquid melting point prediction;Low;J Chem Phys,2020

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