The thermal decomposition mechanism of RDX/AP composites: <i>ab initio</i> neural network MD simulations

Pang, Kehui; Wen, Mingjie; Chang, Xiaoya; Xu, Yabei; Chu, Qingzhao; Chen, Dongping

doi:10.1039/d3cp05709g

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…The corresponding heat rate is 0.27 K/ps, which is feasible to reveal the thermodynamic behaviors of solid propellant . The time step used in this work corresponds to 0.2 fs, which grants excellent numerical stability over the simulation . A Nose–Hoover thermostat is applied with a dump parameter of 20 fs.…”

Section: Methodsmentioning

confidence: 99%

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Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Chang,

Zhang,

Chu

et al. 2024

J. Chem. Theory Comput.

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The machine learning potential has emerged as a promising approach for addressing the accuracy-versus-efficiency dilemma in molecular modeling. Efficiently exploring chemical spaces with high accuracy presents a significant challenge, particularly for the interface reaction system. This study introduces a workflow aimed at achieving this goal by incorporating the classical SOAP descriptor and practical PCA strategy to minimize redundancy and data requirements, while successfully capturing the features of complex potential energy surfaces. Specifically, the study focuses on interface combustion behaviors within promising alloy-based solid propellants. A neural network potential model tailored for modeling AlLi−AP interface reactions under varying conditions is constructed, showcasing excellent predictive capabilities in energy prediction, force estimation, and bond energies. A series of largescale MD simulations reveal that Li doping significantly influences the initial combustion stage, enhancing reactivity and reducing thermal conductivity. Mass transfer analysis also highlights the considerably higher diffusion coefficient of Li compared to Al, with the former being three times greater. Consequently, the overall combustion process is accelerated by approximately 10%. These breakthroughs pave the way for virtual screening and the rational design of advanced propellant formulations and microstructures incorporating alloy-formula propellants.

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Section: Methodsmentioning

confidence: 99%

“…54 The time step used in this work corresponds to 0.2 fs, which grants excellent numerical stability over the simulation. 55 A Nose−Hoover thermostat is applied with a dump parameter of 20 fs. All simulations are conducted using LAMMPS, 56 and visualization is performed using OVITO.…”

Section: Nnp Model and Activementioning

confidence: 99%

Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Chang,

Zhang,

Chu

et al. 2024

J. Chem. Theory Comput.

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Evaluating the thermal stability of chemicals and systems: A review

Andriani,

Pio,

Salzano

et al. 2024

Can J Chem Eng

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In the realm of chemical processing, particularly at the industrial scale, safety is of utmost importance. A predominant factor causing accidents within the chemical industry is runaway phenomena, primarily initiated by uncontrolled exothermic reactions. This review critically examines the often‐overlooked decomposition mechanisms as a significant contributor to thermal energy release, necessitating a comprehensive revision and understanding of both experimental and theoretical strategies for assessing thermal degradation. Key to this discourse is the explication of calorimetry as the principal experimental technique, alongside ab initio quantum chemistry simulations as a robust theoretical framework for quantifying the most relevant properties. However, more than mere cognisance of these methodologies is required for a meticulous thermal stability assessment. The review emphasizes identifying and quantifying fundamental parameters through experimental and theoretical investigations. Only upon acquiring these parameters, including kinetic, thermodynamic, onset, and peak characteristics of the exothermic decomposition reactions, can one effectively mitigate risks and hazards in designing and optimizing chemical processes and apparatus. Furthermore, this review delineates qualitative and quantitative methodologies for hazard assessment, proffering strategies for estimating safe operational conditions and sizing relief devices. The paper culminates in exploring future trajectories in thermal stability assessments, focusing on emerging applications in lithium‐ion batteries, electrolyzers, electrified reactors, ionic liquids, artificial intelligence and machine learning approaches. Thus, the paper underlines the evolving landscape of thermal risk management in contemporary and future chemical industries.

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Comprehensive revelation of the influence mechanism for the thermal decomposition of ammonium perchlorate by ammonium oxalate

Yang,

Kou,

Wei

et al. 2025

International Communications in Heat and Mass Transfer

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The thermal decomposition mechanism of RDX/AP composites: ab initio neural network MD simulations

Abstract: A neural network potential (NNP) is developed to investigate the decomposition mechanism of RDX, AP, and their composites. Utilizing an ab initio dataset, the NNP is evaluated in terms of...

Cited by 3 publications

References 49 publications

Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Evaluating the thermal stability of chemicals and systems: A review

Comprehensive revelation of the influence mechanism for the thermal decomposition of ammonium perchlorate by ammonium oxalate

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