This study employed the reactive force field molecular dynamics to capture atomic-level heat and mass transfer and reaction processes of an aluminum nanoparticle (ANP) oxidizing in a high temperature and pressure oxygen atmosphere, revealing detailed mechanisms for oxidation of ANPs. Temporal variations of temperature, density, mean square displacement, atom consumption rate and heat release rate of ANP have been systematically examined. In addition, the effects of environment on ANP oxidation were also evaluated. The results show that ANP undergoes four stages of preheating, melting, fast Al core and moderate shell oxidations during the whole oxidation process. The Al core starts to melt from core-shell interface with outward diffusion of core Al atoms into the shell. Intense reaction occurs between shell O and core Al atoms around interface at the end of melting, leading to fast Al core oxidation. After complete oxidation of Al core, the oxide shell continues to react with ambient O atoms. Both the initial environmental temperature and the equivalent pressure significantly influence the preheating. Oppositely, the melting stage seems almost independent any of them. While the fast Al core oxidation presents more sensitivity to the ambient equivalent pressure.

A neural network potential (NNP) is developed to investigate the complex reaction dynamics of RDX thermal decomposition. Our NNP model is proven to possess good computational efficiency and retain the...

Ab initio molecular dynamics (AIMD) is an established method for revealing the reactive dynamics of complex systems. However, the high computational cost of AIMD restricts the explorable length and time scales. Here, we develop a fundamentally different approach using molecular dynamics simulations powered by a neural network potential to investigate complex reaction networks. This potential is trained via a workflow combining AIMD and interactive molecular dynamics in virtual reality to accelerate the sampling of rare reactive processes. A panoramic visualization of the complex reaction networks for decomposition of a novel high explosive (ICM-102) is achieved without any predefined reaction coordinates. The study leads to the discovery of new pathways that would be difficult to uncover if established methods were employed. These results highlight the power of neural network-based molecular dynamics simulations in exploring complex reaction mechanisms under extreme conditions at the ab initio level, pushing the limit of theoretical and computational chemistry toward the realism and fidelity of experiments.

This work presents an immersive molecular simulation tool (Manta) based on virtual reality technology, which enables students to explore "real" molecular structures and chemical reactions on-the-fly. Manta can work as a virtual classroom and even a playground for chemistry education, where students learn the progress of chemical reactions and identify the key impacting factors by interacting with the molecular structures. We conducted class experiments with 16 undergraduate and 18 graduate students on hydrogen combustion reactions to illustrate the educational performance of Manta. Three tasks are designed to learn the roles of temperature and collision characteristics for chemical reactions as well as the elementary reactions in hydrogen combustion. The students' comments showed that the learning experience using Manta was much appreciated compared to the traditional approach. Thus, we believe that Manta could serve as a good tool in chemistry courses and promote students' understanding of macroscopic phenomena from the perspective of molecular motions.

The progress of surface reactions can be largely impacted by anisotropic energy transfer. Here, we carried out reactive molecular dynamic simulations on aluminum nanoparticles in shock waves up to 8 km/s. From the analysis of particle morphological evolutions, heat and mass transfer, and reaction products, it is found that the shock-induced effect strongly correlates with flow velocity. We further elaborate oxidation mechanisms into three modes: diffusion oxidation (<2 km/s), anisotropic oxidation (2–5 km/s), and microexplosion oxidation (>5 km/s). The first mode corresponds to the typical isotropic mechanism of nanoparticles. In the second mode, shock induces an anisotropic temperature gradient via molecular collisions and triggers the ignition in one side. Further increasing the flow velocity, severe dispersion of small Al x O y clusters is identified as a microexplosion event. These three oxidation modes dedicate to interpret the effect of translational energy on surface reactions and supplement the current oxidation theory.

A lack of clarity in the reaction mechanism of the aluminum nanoparticle (ANP) severely restricts its effective applications. By describing the physicochemical evolution of ANP burning in typical oxidizers (CO 2 , H 2 O, and O 2 ) at the nanoscale, three principal reaction modes including physical adsorption, chemical adsorption, and reactive diffusion were captured during the reaction. Initially, oxidizer molecules are physically and chemically adsorbed on the ANP surface until ignition in which reaction heat plays a more important role in contrast to heat transfer. Subsequently, partial oxidizer atoms adsorbed by surface diffuse across the shell to react with the Al core, presenting the dominant mode of reactive diffusion. It is assumed that the binding energy between Al and oxidizer atoms is in an inverse relation to atomic diffusivity but is positively correlated to reaction heat, resulting in various ANP structures and heat release rates. Our findings provide design guidelines to control various oxidizer supplies with respect to the reaction stages to balance the energy release and the residence time of ANP.

A large number of PAH molecules is collected from recent literature. The HOMO-LUMO gap value of PAHs was computed at the level of B3LYP/6-311+G (d,p). The gap values lie in the range of 0.64–6.59 eV. It is found that the gap values of all PAH molecules exhibit a size dependency to some extent. However, the gap values may show a big variation even at the same size due to the complexity in the molecular structure. All collected PAHs are further classified into seven groups according to features in the structures, including the types of functional groups and the molecular planarity. The impact of functional groups, including –OH, –CHO, –COOH, =O, –O– and –CnHm on the bandgap is discussed in detail. The substitution of ketone group has the greatest reduction on the HOMO-LUMO gap of PAH molecules. Besides functional groups, we found that both local structure and the position of five-member rings make critical impacts on the bandgap via a detailed analysis of featured PAHs with unexpected low and high gap values. Among all these factors, the five-member rings forming nonplanar PAHs impact the gap most. Furthermore, we developed a machine learning model to predict the HOMO-LUMO gaps of PAHs, and the average absolute error is only 0.19 eV compared with the DFT calculations. The excellent performance of the machine learning model provides us an accurate and efficient way to explore the band information of PAHs in soot formation.

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