Uncovering the decomposition mechanism of nitrate ester plasticized polyether (NEPE): a neural network potential simulation.

Nitrate ester plasticized polyether (NEPE) propellants have attracted widespread attention due to their high energy density and excellent low-temperature mechanical properties. However, little is known about the thermal decomposition process of the NEPE propellant, particularly lacking microscale models and interaction mechanisms. This work aims to establish a high-precision and efficient neural network potential (NNP) model covering the NEPE matrix, describing its mechanical behavior and detailed thermal decomposition mechanisms. The model accuracy, including atomic energies and forces, was validated through density functional theory (DFT) results, and the NEPE propellant decomposition model was verified via molecular dynamics (MD) simulations with DFT precision. The results demonstrate that the NNP model accurately predicts the energies and forces of the NEPE matrix for single and mixed systems at the DFT-level precision, and reproduces the mechanical properties consistent with DFT calculations. Meanwhile, the thermal decomposition order of the NEPE matrix predicted by NNP is consistent with the experimental results, accurately capturing complex physical phenomena and detailed decomposition processes among components. It is also revealed that the addition of a binder can improve the stability of the propellant and extend its energy release time. This study applies innovative machine learning algorithms to develop an NNP computational model for the NEPE matrix with DFT precision, which is crucial for practical propellant formulation design.

Determining the mechanical and decomposition properties of high energetic materials (α-RDX, ß-HMX, and ε-CL-20) using a neural network potential.

Molecular simulations of high energetic materials (HEMs) are limited by efficiency and accuracy. Recently, neural network potential (NNP) models have achieved molecular simulations of millions of atoms while maintaining the accuracy of density functional theory (DFT) levels. Herein, an NNP model covering typical HEMs containing C, H, N, and O elements is developed. The mechanical and decomposition properties of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by employing the molecular dynamics (MD) simulations based on the NNP model. The calculated results show that the mechanical properties of α-RDX, ß-HMX, and ε-CL-20 agree with previous experiments and theoretical results, including cell parameters, equations of state, and elastic constants. In the thermal decomposition simulations, it is also found that the initial decomposition reactions of the three crystals are N-NO2 homolysis, corresponding radical intermediates formation, and NO2-induced reactions. This decomposition trajectory is mainly divided into two stages separating from the peak of NO2: pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements in this work is an alternative reactive force field for RDX, HMX, and CL-20 HEMs, and it opens up new potential for future kinetic study of nitramine explosives.

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

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 atomic energy and forces, demonstrating strong agreement with ab initio calculations. Numerical stability tests across a range of timesteps reveal excellent stability compared to the state-of-the-art ReaxFF models. Then the thermal decomposition of pure RDX, AP, and RDX/AP composites is performed using NNP to explore the coupling effect between RDX and AP. The results highlight a dual interaction between RDX and AP, i.e., AP accelerates RDX decomposition, particularly at low temperatures, and RDX promotes AP decomposition. Analyzing RDX trajectories at the RDX/AP interface unveils a three-part decomposition mechanism involving N-N bond cleavage, H transfer with AP to form Cl-containing acid, and chain-breaking reactions generating small molecules such as N2, CO, and CO2. The presence of AP enhances H transfer reactions, contributing to its role in promoting RDX decomposition. This work studies the reaction kinetics of RDX/AP composites from the atomic point of view, and can be widely used in the establishment of reaction kinetics models of composite systems with energetic materials.

Monitoring the melting behavior of boron nanoparticles using a neural network potential.

The melting behavior of metal additives is fundamental for various propulsion and energy-conversion applications. A neural network potential (NNP) is proposed to examine the size-dependent melting behaviors of boron nanoparticles. Our NNP model is proven to possess a desirable computational efficiency and retain ab initio accuracy, allowing investigation of the physicochemical properties of bulk boron crystals from an atomic perspective. In this work, a series of NNP-based molecular dynamics simulations were conducted and numerical evidence of the size-dependent melting behavior of boron nanoparticles with diameters from 3 to 6 nm was reported for the first time. Evolution of the intermolecular energy and the Lindemann index are used to monitor the melting process. A liquid layer forms on the particle surface and further expands with increased temperature. Once the liquid layer reaches the core region, the particle is completely molten. The reduced melting temperature of the boron nanoparticle decreases with its particle size following a linear relationship with reciprocal size, similar to other commonly used metals (Al and Mg). Additionally, boron nanoparticles are more sensitive to particle size than Al particles and less sensitive than Mg particles. These findings provide an atomistic perspective for developing manufacturing techniques and tailoring combustion performance in practical applications.

Revealing the thermal decomposition mechanism of RDX crystals by a neural network potential.

A neural network potential (NNP) is developed to investigate the complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX) thermal decomposition. Our NNP model is proven to possess good computational efficiency and retain the ab initio accuracy, which allows the investigation of the entire decomposition process of bulk RDX crystals from an atomic perspective. A series of molecular dynamics (MD) simulations are performed on the NNP to calculate the physical and chemical properties of the RDX crystal. The results show that the NNP can accurately describe the physical properties of RDX crystals, such as the cell parameters and the equation of state. The simulations of RDX thermal decomposition reveal that the NNP could capture the evolution of species at ab initio accuracy. The complex reaction network was established, and a reaction mechanism of RDX decomposition was provided. The N-N homolysis is the dominant channel, which cannot be observed in previous DFT studies of isolated RDX molecule. In addition, the H abstraction reaction by NO2 is found to be the critical pathway for NO and H2O formation, while the HONO elimination is relatively weak. The NNP gives an atomic insight into the complex reaction dynamics of RDX and can be extended to investigate the reaction mechanism of novel energetic materials.

Optical Properties of Polycyclic Aromatic Hydrocarbon Clusters with Oxygenated Substitutions: A Theoretical Analysis.

The introduction of functional groups at high coverage levels can have significant impacts on the band structures of polycyclic aromatic hydrocarbon (PAH) clusters. The HOMO-LUMO gaps are highly sensitive to the type and distribution of functional groups. An in-house method is proposed to build PAH (naphthalene, pyrene, coronene, and ovalene) clusters with surface functionalization of -OH, -COOH and -CHO groups using the DFT method. The -CHO groups are found to reduce the gap value the most, but exceptions exist due to the spatial distribution of functional groups. Considering the impact of -CHO groups only, we can approximate that the impact of functional groups lies in the range of 0.14-0.89 eV. Applying further analysis on the possible energy number of energy transitions of substituted PAH clusters, it is shown that PAH clusters with oxygenated functions still behave like an indirect band gap material. The coupling effect of PAH stacking and PAH size is also addressed. A simple expression is proposed to estimate the bandgap of a mixed system using the HOMO and LUMO energy of the two components. Further attempts are made to interpret recent experiments from the impact of PAH stacking, PAH size, and functional groups.

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

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 large-scale 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.

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

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.

In Situ Synthesis of 3D Flower-Like Nanocrystalline Ni/C and its Effect on Hydrogen Storage Properties of LiAlH4.

Lithium alanate (LiAlH4 ) is of particular interest as one of the most promising candidates for solid-state hydrogen storage. Unfortunately, high dehydrogenation temperatures and relatively slow kinetics limit its practical applications. Herein, 3D flower-like nanocrystalline Ni/C, composed of highly dispersed Ni nanoparticles and interlaced carbon flakes, was synthesized in situ. The as-synthesized nanocrystalline Ni/C significantly decreased the dehydrogenation temperature and dramatically improved the dehydrogenation kinetics of LiAlH4 . It was found that the LiAlH4 sample with 10 wt % Ni/C (LiAlH4 -10 wt %Ni/C) began hydrogen desorption at approximately 48 °C, which is very close to ambient temperature. Approximately 6.3 wt % H2 was released from LiAlH4 -10 wt %Ni/C within 60 min at 140 °C, whereas pristine LiAlH4 only released 0.52 wt % H2 under identical conditions. More importantly, the dehydrogenated products can partially rehydrogenate at 300 °C under 4 MPa H2 . The synergetic effect of the flower-like carbon substrate and Ni active species contributes to the significantly reduced dehydrogenation temperatures and improved kinetics.

Improved Dehydrogenation Properties of LiBH4 Using Catalytic Nickel- and Cobalt-based Mesoporous Oxide Nanorods.

Lithium borohydride (LiBH4 ) with a theoretical hydrogen storage capacity of 18.5 wt % has attracted intense interest as a high-density hydrogen storage material. However, high dehydrogenation temperatures and limited kinetics restrict its practical applications. In this study, mesoporous nickel- and cobalt-based oxide nanorods (NiCo2 O4 , Co3 O4 and NiO) were synthesized in a controlled manner by using a hydrothermal method and then mixed with LiBH4 by ball milling. It is found that the dehydrogenation properties of LiBH4 are remarkably enhanced by doping the as-synthesized metal oxide nanorods. When the mass ratio of LiBH4 and oxides is 1:1, the NiCo2 O4 nanorods display the best catalytic performance owing to the mesoporous rod-like structure and synergistic effect of nickel and cobalt active species. The initial hydrogen desorption temperature of the LiBH4 -NiCo2 O4 composite decreases to 80 °C, which is 220 °C lower than that of pure LiBH4 , and 16.1 wt % H2 is released at 500 °C for the LiBH4 -NiCo2 O4 composite. Meanwhile, the composite also exhibits superior dehydrogenation kinetics, which liberates 5.7 wt % H2 within 60 s and a total of 12 wt % H2 after 5 h at 400 °C. In comparison, pure LiBH4 releases only 5.3 wt % H2 under the same conditions.

Facile Synthesis of A 3D Flower-Like Mesoporous Ni@C Composite Material for High-Energy Aqueous Asymmetric Supercapacitors.

A 3D flower-like mesoporous Ni@C composite material has been synthesized by using a facile and economical one-pot hydrothermal method. This unique 3D flower-like Ni@C composite, which exhibited a high surface area (522.4 m2 g-1 ), consisted of highly dispersed Ni nanoparticles on mesoporous carbon flakes. The effect of calcination temperature on the electrochemical performance of the Ni@C composite was systematically investigated. The optimized material (Ni@C 700) displayed high specific capacity (1306 F g-1 at 2 A g-1 ) and excellent cycling performance (96.7 % retention after 5000 cycles). Furthermore, an asymmetric supercapacitor (ASC) that contained Ni@C 700 as cathode and mesoporous carbon (MC) as anode demonstrated high energy density (60.4 W h kg-1 at a power density of 750 W kg-1 ).

MOF based on a longer linear ligand: electrochemical performance, reaction kinetics, and use as a novel anode material for sodium-ion batteries.

A metal-organic framework based on a longer linear ligand was rationally designed and evaluated as a novel anode material for sodium-ion batteries. It delivered a high specific capacity of 269 mA h g-1 with a desired voltage plateau and demonstrated excellent capacity retention (79.0% after 1000 cycles). In addition, its reaction kinetics was also investigated in detail by performing cyclic voltammetry and a predominantly diffusion-controlled process was clearly revealed.