Quasiplastic deformation in shocked nanocrystalline boron carbide: Grain boundary sliding and local amorphization

Li, Jun; An, Qi

doi:10.1016/j.jeurceramsoc.2022.10.014

Cited by 19 publications

(13 citation statements)

References 53 publications

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“…The transferability of force fields in mechanical investigations is related to the equation of state (EOS). Li and An constructed a data set encompassing scale factors ranging from 0.8 to 1.0 and successfully replicated the quasi-plastic deformation of boron carbide observed in experiments. This study demonstrates the adequacy of the EOS data set in investigating various mechanical properties.…”

Section: Resultsmentioning

confidence: 99%

Ab Initio Driven Exploration on the Thermal Properties of Al–Li Alloy

Chang,

Wu,

Chu

et al. 2024

ACS Appl. Mater. Interfaces

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Al−Li alloys are feasible and promising additives in advanced energy and propellant systems due to the significantly enhanced heat release and increased specific impulse. The thermal properties of Al−Li alloys directly determine the manufacturing, storage safety, and ignition delay of propellants. In this study, a neural network potential (NNP) is developed to investigate the thermal behaviors of Al−Li alloys from an atomistic perspective. The novel NNP demonstrates an excellent predictive ability for energy, atomic force, mechanical behaviors, phonon vibrations, and dynamic evolutions. A series of NNP-based molecular dynamics simulations are performed to investigate the effect of Li doping on the thermal properties of Al−Li alloys. All calculated results for Al−Li alloys are consistent with experimental values for Al, ensuring their validity in predicting Al−Li interactions. The simulation results suggest that a minor increment in the Li content results in a slight change in the melting point, thermal expansion, and radical distribution functions. These three properties are associated with the lattice characteristics; nonetheless, it causes a substantial reduction in thermal conductivity, which is related to the physical properties of the elements. The lower thermal conductivity allows heat accumulation on the particle surface, thereby speeding up the surface premelt and ignition. This provides an alternative atomic explanation for the improved combustion performance of Al−Li alloys. These findings integrate insights from the field of alloy material science into crucial combustion applications, serving as an atomistic guide for developing manufacturing techniques.

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Section: Resultsmentioning

confidence: 99%

Ab Initio Driven Exploration on the Thermal Properties of Al–Li Alloy

Chang,

Wu,

Chu

et al. 2024

ACS Appl. Mater. Interfaces

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“…However, the high computational costs of density functional theory (DFT) calculations limit their applications in molecular systems, whose unit cells usually contain dozens or even hundreds of atoms. Recently developed machine learning potentials (MLPs), such as the Gaussian approximation potential (GAP) [1][2][3][4][5][6] , moment tensor potential (MTP) [7][8][9] , and DeepMD-kit package [10][11][12][13] , can perform DFT-level calculations at the computational cost of classical force fields. CSP methods, such as Universal Structure Predictor: Evolutionary Xtallography (USPEX) [14,15] and Crystal structure AnaLYsis by Particle Swarm Optimization (CALPSO) [16] , combining MLPs [17][18][19][20][21][22][23] will undoubtedly become the main approaches in the search for new materials.…”

Section: Introductionmentioning

confidence: 99%

Searching new cocrystal structures of CL-20 and HMX via evolutionary algorithm and machine learning potential

Ye,

Guo,

Chai

et al. 2024

Journal of Materials Informatics

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In this work, we report the discovery of energy cocrystals using an efficient iterative workflow combining an evolutionary algorithm and a machine learning potential (MLP). The compound 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) has attracted significant attention owing to its higher energy density than traditional energetic materials. However, the higher sensitivity has limited its applications. An important way to reduce its sensitivity involves cocrystal engineering with traditional explosives. Many cocrystal structures are expected to be composed of these two components. We developed an efficient iterative workflow to explore the phase space of CL-20 and 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) cocrystals using an evolutionary algorithm and an MLP. The algorithm was based on the Universal Structure Predictor: Evolutionary Xtallography (USPEX) software, and the MLP was the reactive force field with neural networks (ReaxFF-nn ) model. A set of high-density cocrystal structures was produced through this workflow; these structures were further checked via first-principles geometry optimizations. After careful screening, we identified several high-density cocrystal structures with densities of up to 1.937 g/cm3 and HMX: CL-20 ratios of 1:1 and 1:2. The influence of hydrogen bonds on the formation of high-density cocrystals was also discussed, and a roughly linear relationship was found between energy and density.

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“…Incorporating aluminum particles into high explosives (HE) has been demonstrated to increase the total energy and work capacity, thus representing a pivotal avenue of investigation for enhancing the energy performance of explosives [4][5][6][7][8]. Therefore, researches have been dedicated to elucidating the energy release mechanism of aluminized explosives spanning over a century [1,[8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Activation and reaction mechanism of nano‐aluminized explosives under shock wave

Wang,

Xiao,

Chen

et al. 2024

Propellants Explo Pyrotec

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To investigate the effect of aluminum (Al) nanoparticles on the energy release mechanism of high explosives, a comprehensive analysis was conducted on the mechanical response and chemical reaction mechanism of pure 1,3,5‐Trinitro‐1,3,5‐triazinane (RDX) and nano‐aluminized RDX across varying particle velocities using molecular dynamics simulation. The simulation results show that the velocity of the shock wave which is formed in the explosive increases as the velocity of the particle increases. Notably, detonation was absent when the particle velocity was below 3 km/s, but prominently observed beyond this threshold, accompanied by a diminishing delay in reaction time for aluminum particles as particle velocity increased. After detonation, a localized pressure reduction behind aluminum particles was observed, elucidating the diminished detonation efficacy of aluminized explosives. Furthermore, the introduction of aluminum particles led to a deceleration in the RDX reaction rate, with the emergence of aluminum atomic clusters highlighting previously overlooked gas‐phase reactions that necessitate inclusion in detonation modeling for aluminized explosives.

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Quasiplastic deformation in shocked nanocrystalline boron carbide: Grain boundary sliding and local amorphization

Cited by 19 publications

References 53 publications

Ab Initio Driven Exploration on the Thermal Properties of Al–Li Alloy

Ab Initio Driven Exploration on the Thermal Properties of Al–Li Alloy

Searching new cocrystal structures of CL-20 and HMX via evolutionary algorithm and machine learning potential

Activation and reaction mechanism of nano‐aluminized explosives under shock wave

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