Harnessing machine learning enabled quickly predicting density of CHON molecules for discovering new energetic materials DOI Creative Commons

Ruoxu Zong,

Zi Li, Ziyu Hu

et al.

AIP Advances, Journal Year: 2025, Volume and Issue: 15(4)

Published: April 1, 2025

The application of machine learning in the research and development energetic materials is becoming increasingly widespread for performance prediction inverse design. Many advances have been achieved, especially discovery various new materials. However, main properties such as data acquisition, molecular characterization, limitations objects insufficient. Density, a critical factor influencing detonation materials, difficult to predict with high precision speed at large scale. In this study, techniques are employed density CHNO result explore simultaneously possessing stability. By screening dataset 16 548 candidate molecules, 175 potential high-performance molecules were identified. Among candidates, it noted that molecule velocity 7.328 Km/s pressure 24.48 GPa was which comparable TNT. study shows transformative accelerating novel vital diverse applications optimized expected accelerate next-generation

Language: Английский

Harnessing machine learning enabled quickly predicting density of CHON molecules for discovering new energetic materials DOI Creative Commons

Ruoxu Zong,

Zi Li, Ziyu Hu

et al.

AIP Advances, Journal Year: 2025, Volume and Issue: 15(4)

Published: April 1, 2025

The application of machine learning in the research and development energetic materials is becoming increasingly widespread for performance prediction inverse design. Many advances have been achieved, especially discovery various new materials. However, main properties such as data acquisition, molecular characterization, limitations objects insufficient. Density, a critical factor influencing detonation materials, difficult to predict with high precision speed at large scale. In this study, techniques are employed density CHNO result explore simultaneously possessing stability. By screening dataset 16 548 candidate molecules, 175 potential high-performance molecules were identified. Among candidates, it noted that molecule velocity 7.328 Km/s pressure 24.48 GPa was which comparable TNT. study shows transformative accelerating novel vital diverse applications optimized expected accelerate next-generation

Language: Английский

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