Promising Energetic Melt-Castable Material with Balanced Properties DOI
Fang Chen, Yi Wang, Siwei Song

et al.

ACS Applied Materials & Interfaces, Journal Year: 2023, Volume and Issue: 15(20), P. 24408 - 24415

Published: May 15, 2023

As one of the most widely used energetic materials to date, trinitrotoluene (TNT) suffers from several generally known drawbacks such as high toxicity, oil permeability, and poor mechanical properties, which are driving researchers explore new high-performance melt-castable for replacing TNT. However, it still remains a great challenge discover promising TNT alternative due multidimensional requirements practical applications. Herein, we reported molecule, 4-methoxy-1-methyl-3,5-dinitro-1H-pyrazole (named DMDNP). Besides reasonable melting point (Tm: 94.8 °C), good thermostability (Td: 293.2 excellent chemical compatibility, DMDNP exhibits some obvious advantages over including more environmentally friendly synthesis, yield, low volume shrinkage, electrostatic sensitivities, etc., demonstrating well-balanced properties promise replacement.

Language: Английский

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

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 486, P. 150410 - 150410

Published: March 14, 2024

Language: Английский

Citations

24
Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives DOI Creative Commons

Xiaolan Tian,

Siwei Song, Fang Chen

et al.

Energetic Materials Frontiers, Journal Year: 2022, Volume and Issue: 3(3), P. 177 - 186

Published: Aug. 18, 2022

Predicting chemical properties is one of the most important applications machine learning. In recent years, prediction energetic materials using learning has been receiving more attention. This review summarized advances in predicting compounds' (e.g., density, detonation velocity, enthalpy formation, sensitivity, heat explosion, and decomposition temperature) Moreover, it presented general steps for applying to practical from aspects data, molecular representation, algorithms, accuracy. Additionally, raised some controversies specific its possible development directions. Machine expected become a new power driving soon.

Language: Английский

Citations

51
Descriptors applicability in machine learning-assisted prediction of thermal decomposition temperatures for energetic materials: Insights from model evaluation and outlier analysis DOI
Zhixiang Zhang, Chao Chen,

Yilin Cao

et al.

Thermochimica Acta, Journal Year: 2024, Volume and Issue: 735, P. 179717 - 179717

Published: March 6, 2024

Language: Английский

Citations

9
Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials DOI Creative Commons
Siwei Song, Yi Wang, Fang Chen

et al.

Engineering, Journal Year: 2022, Volume and Issue: 10, P. 99 - 109

Published: Feb. 24, 2022

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error. Herein, methodology combining domain knowledge, machine learning algorithm, experiments presented for accelerating the discovery of novel materials. A high-throughput virtual screening (HTVS) system integrating on-demand molecular generation models covering prediction crystal packing mode scoring established. With proposed HTVS system, candidate molecules promising desirable are rapidly targeted from generated space containing 25 112 molecules. Furthermore, study structure shows that good comprehensive performances target molecule agreement predicted results, thus verifying effectiveness methodology. This work demonstrates new paradigm discovering can be extended other organic without manifest obstacles.

Language: Английский

Citations

32
Advancements in methodologies and techniques for the synthesis of energetic materials: A review DOI Creative Commons
Wei Du, Lei Yang, Jing Feng

et al.

Energetic Materials Frontiers, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Recent years have witnessed significant advancements in methodologies and techniques for the synthesis of energetic materials, which are expected to shape future manufacturing applications. Techniques including continuous flow chemistry, electrochemical synthesis, microwave-assisted biosynthesis been extensively employed pharmaceutical fine chemical industries and, gratifyingly, found broader This review comprehensively introduces recent utilization these emerging techniques, aiming provide a catalyst development novel green methods synthesizing materials.

Language: Английский

Citations

6
A new nitrate-based energetic molecular perovskite as a modern edition of black powder DOI
Shao‐Li Chen, Yu Shang, Jun Jiang

et al.

Energetic Materials Frontiers, Journal Year: 2022, Volume and Issue: 3(3), P. 122 - 127

Published: July 16, 2022

Language: Английский

Citations

20
Combination of Nitrogen-Rich Skeleton and Coordination Group: Synthesis of a High-Energy Primary Explosive Based on 1H-Tetrazole-5-Carbohydrazide DOI Creative Commons
Tingwei Wang, Zujia Lu,

Shu Bu

et al.

Defence Technology, Journal Year: 2023, Volume and Issue: 31, P. 271 - 277

Published: Feb. 25, 2023

The high energy coordination compounds Cu(TZCA)2(ClO4)2 (ECCs-1) was prepared by 1H-tetrazole-5-carbohydrazide (TZCA) with a skeleton and strong ability group. At the same time, reaction activity of ligand explored, single crystal structure it intermediate were obtained. structures all substances characterized IR EA. And composition ECCs-1 are confirmed ESP, AC, SEM ICP-OES. Physical chemical properties tests show that has an acceptable thermal stability (Td = 177°C) extremely sensitive mechanical stimulation (IS 1 J, FS 5 N). comprehensive performance test results excellent initiation ability. In addition, decomposition mechanism is explored from two aspects experiment theoretical calculation.

Language: Английский

Citations

13
Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis DOI Creative Commons
Zhixiang Zhang, Yilin Cao, Chao Chen

et al.

Energetic Materials Frontiers, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 1, 2023

In this study, machine learning (ML)-assisted regression modeling was conducted to predict the thermal decomposition temperatures and explore factors that correlate with stability of energetic materials (EMs). The performed based on a dataset consisting 885 various compounds using linear nonlinear algorithms. tree-based models established demonstrated acceptable predictive abilities, yielding low mean absolute error (MAE) 31°C. By analyzing through hierarchical classification, study insightfully identified affecting EMs' temperatures, overall accuracy improved targeted modeling. SHapley Additive exPlanations (SHAP) analysis indicated descriptors such as BCUT2D, PEOE_VSA, MolLog_P, TPSA played significant role, demonstrating process is influenced by multiple relating composition, electron distribution, chemical bond properties, substituent type molecules. Additionally, Carbon_contents Oxygen_Balance proposed for characterizing EMs showed strong correlations temperatures. trends their SHAP values most suitable ranges were 0.2–0.35 −65 −55, respectively. Overall, shows potential ML temperature prediction provides insights into characteristics molecular descriptors.

Language: Английский

Citations

12
Bionic inspired multifunctional modular energetic materials: an exploration of new generation of application-oriented energetic materials DOI
Yujia Wen, Linyuan Wen, Bojun Tan

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(16), P. 9427 - 9437

Published: Jan. 1, 2024

Aiming to balance the pertinence and universality of energetic materials, this study proposes a new concept bionic inspired multifunctional modular materials seeks out potential monomers via high-throughput screening strategy.

Language: Английский

Citations

4
Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials DOI Creative Commons

Qiaolin Gou,

Jing Liu,

Haoming Su

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109452 - 109452

Published: March 8, 2024

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has a lack quick accurate method for evaluating stability diverse EMs. Here, we develop machine learning prediction model with high accuracy bond dissociation (BDE) A reliable representative BDE dataset EMs is constructed by collecting 778 experimental compounds quantum mechanics calculation. To sufficiently characterize EMs, hybrid feature representation proposed coupling local target into global structure characteristics. alleviate limitation dataset, pairwise difference regression utilized as data augmentation advantage reducing systematic errors improving diversity. Benefiting from these improvements, XGBoost achieves best R2 0.98 MAE 8.8 kJ mol−1, significantly outperforming competitive models.

Language: Английский

Citations

4