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

Ruoxu Zong,

Zi Li, Ziyu Hu

и другие.

AIP Advances, Год журнала: 2025, Номер 15(4)

Опубликована: Апрель 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

Язык: Английский

Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry DOI Creative Commons
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan

и другие.

Artificial Intelligence Chemistry, Год журнала: 2024, Номер 2(1), С. 100049 - 100049

Опубликована: Янв. 19, 2024

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI's pivotal roles field organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies planning, accelerates catalyst discovery, and fuels material innovation so on. It seamlessly integrates data-driven algorithms with intuition to redefine As chemistry advances, it promises accelerated research, sustainability, innovative solutions chemistry's pressing challenges. The fusion poised shape field's future profoundly, offering new horizons precision efficiency. encapsulates transformation marking moment where data converge revolutionize world molecules.

Язык: Английский

Процитировано

31

Synthesis, design and development of energetic materials: Quo Vadis? DOI
Nikita V. Muravyev, Леонид Л. Ферштат, Qinghua Zhang

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер 486, С. 150410 - 150410

Опубликована: Март 14, 2024

Язык: Английский

Процитировано

24

LSTM-BP neural network analysis on solid-liquid phase change in a multi-channel thermal storage tank DOI
Tian Xiao, Zhengguang Liu, Liu Lu

и другие.

Engineering Analysis with Boundary Elements, Год журнала: 2022, Номер 146, С. 226 - 240

Опубликована: Окт. 30, 2022

Язык: Английский

Процитировано

62

Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives DOI
Júlio César Duarte,

Romulo Dias da Rocha,

Itamar Borges

и другие.

Physical Chemistry Chemical Physics, Год журнала: 2023, Номер 25(9), С. 6877 - 6890

Опубликована: Янв. 1, 2023

Machine learning was used to rationalize the molecular origin of impact sensitivity nitroaromatic explosives.

Язык: Английский

Процитировано

25

Fluorescence sensor array based on covalent organic frameworks and QSAR study for identification of organic pesticides DOI

Fangxia An,

Fang Li, Shengyuan Deng

и другие.

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 112986 - 112986

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

2D flame temperature and soot concentration reconstruction from partial discrete data via machine learning: A case study DOI Creative Commons
Mingfei Chen,

R. Zheng,

Xuan Zhao

и другие.

Case Studies in Thermal Engineering, Год журнала: 2025, Номер unknown, С. 106005 - 106005

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1

Dual-channel MIRECL portable devices with impedance effect coupled smartphone and machine learning system for tyramine identification and quantification DOI
Zhiwei Lu, Jun Qin, Wu Chun

и другие.

Food Chemistry, Год журнала: 2023, Номер 429, С. 136920 - 136920

Опубликована: Июль 20, 2023

Язык: Английский

Процитировано

19

Energetic 1,2,4-oxadiazoles: synthesis and properties DOI
Alexander V. Shaferov, Леонид Л. Ферштат

Russian Chemical Reviews, Год журнала: 2024, Номер 93(2), С. RCR5109 - RCR5109

Опубликована: Янв. 16, 2024

The study of high-energy materials based on poly nitrogen- and nitrogen-oxygen-containing heterocycles is one the most important relevant modern interdisciplinary research areas at intersection organic physical chemistry science. Among such heterocycles, 1,2,4-oxadiazole ring a rather interesting building block for synthesis new energetic compounds. Although 1,2,4-oxadiazoles has been developed more than 100 years, these have only recently become known are currently "hot spots" in this field This review systematizes published methods features reactivity 1,2,4-oxadiazole-based Mono- bis(1,2,4-oxadiazoles) as well structures containing other azoles pyrazole, 1,2,5-oxadiazole, 1,3,4-oxadiazole, 1,2,3-triazole, 1,2,4-triazole tetrazole considered. For series structurally similar compounds, their physicochemical properties summarized factors affecting particular parameter discussed.<br> Bibliography — 123 references.

Язык: Английский

Процитировано

8

Insights into Preparation Methods and Functions of Carbon-Based Solid Acids DOI Creative Commons
Shu Dong, Jian Zhang, Roger Ruan

и другие.

Molecules, Год журнала: 2024, Номер 29(1), С. 247 - 247

Опубликована: Янв. 3, 2024

With the growing emphasis on green chemistry and ecological environment, researchers are increasingly paying attention to greening materials through use of carbon-based solid acids. The diverse characteristics acids can be produced different preparation conditions modification methods. This paper presents a comprehensive summary current research progress acids, encompassing common carbonization methods, such as one-step, two-step, hydrothermal, template composition carbon source material may main factor affecting its method temperature. Additionally, acidification types including sulfonating agent, phosphoric acid, heteropoly nitric acid explored. Furthermore, functions in esterification, hydrolysis, condensation, alkylation thoroughly analyzed. study concludes by addressing existing drawbacks outlining potential future development prospects for context their important role sustainable environmental preservation.

Язык: Английский

Процитировано

7

Machine learning based technique for outlier detection and result prediction in combustion diagnostics DOI
Mingfei Chen, Kaile Zhou, Dong Liu

и другие.

Energy, Год журнала: 2024, Номер 290, С. 130218 - 130218

Опубликована: Янв. 4, 2024

Язык: Английский

Процитировано

7