Hydration of Calcium Oxide Surface Predicted by Reactive Force Field Molecular Dynamics

To investigate the antioxidant mechanisms of phenolic antioxidants in base oils, this study employed the reactive force field (ReaxFF) molecular dynamics (MD) simulations to reveal the reaction pathways and product distributions of trihydroxymethylpropyl trioleate (TMPTO, base oil) and antioxidants AO-80 and XH-245 under high-temperature conditions. A multi-output regression prediction model (MORP) was developed to achieve accurate prediction of carbon atom types and chemical bond types in the reaction systems. Furthermore, a chemical reaction prediction model (CRPM) based on graph neural networks (GNN) was developed to predict the product types and distributions at different times during the antioxidant process of lubricating oils. The results show that the oxidative decomposition of TMPTO is characterised by the cleavage of C–C bonds and C–O bonds. The main small-molecule oxidation products include CO2, C2H4, H2O, CO, formaldehyde, formic acid, and other C2 and C3 compounds. The ArOH groups in phenolic antioxidant molecules can delay the oxidative cleavage process of base oil through hydrogen transfer steps. The system with the two types of antioxidants exhibits better antioxidant effects than the single antioxidant system under the same conditions. The MORP and CRPM models provide good prediction results for carbon atom types, chemical bond types, and product distributions.

Theoretical study of the CO formation mechanism in the CO2 gasification of lignite

Exploring carbon dioxide generation in combustion of long-flame coal in Huating mining area by using ReaxFF MD

The interaction of water molecules on a nickel surface was studied using ReaxFF (reactive force field) molecular dynamics. This approach was originally developed by van Duin et al. to study the hydrocarbon chemistry and the catalytic properties of organic compounds. To our knowledge, this method has not been used to study the corrosion processes of nickel exposed to water, which is what we set out to achieve in the present investigation. To do so, calculations were first performed using ReaxFF in order to reproduce certain well-known properties of pure nickel and nickel-water systems. This allowed us to study the adsorption of a single water molecule interacting with an optimized nickel surface. We also investigated the interaction of 405 molecules of water (ρ=0.99 g.cm-3) on the (100), (110) and (111) surfaces of a single crystal of nickel at 300 K. The results show that a water bilayer is adsorbed on nickel surfaces: the first water layer is directly bonded to the surface, whereas the molecules in the first and second layers are held together by hydrogen bonds.

Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics

Reactive force field (ReaxFF) molecular dynamics investigation of bituminous coal combustion under oxygen-deficient conditions

In computational catalysis, density-functional theory (DFT) calculations are usually utilized, although they suffer from high computational costs. Thus, it would be challenging to explicitly predict the catalytic properties of nanoparticles (NPs) at the nanoscale under solvents. Using molecular dynamics (MD) simulations with a reactive force field (ReaxFF), we investigated the catalytic performance of Ni-Pt NPs for the direct synthesis of hydrogen peroxide (H2O2), in which water solvents were explicitly considered along with the effects of the sizes (1.5, 2.0, 3.0, and 3.5 nm) and compositions (Ni90Pt10, Ni80Pt20, and Ni50Pt50) of the NPs. Among the Ni-Pt NPs, 3.0 nm NPs show the highest activity and selectivity for the direct synthesis of H2O2, revealing that the catalytic performance is not well correlated with the surface areas of NPs. The superior catalytic performance results from the high H2 dissociation and low O2 dissociation properties, which are correlated with the numbers of NiNiPt-fcc and NiNi-bridge sites on the surface of Ni-Pt NPs, respectively. The ReaxFF-MD simulations propose the optimum composition (Ni80Pt20) of 3.0 nm Ni-Pt NPs, which is also explained by the numbers of NiNiPt-fcc and NiNi-bridge sites. Furthermore, from the ReaxFF-MD simulations, the direct synthesis of H2O2 for the Ni-Pt NPs can be achieved not only with the Langmuir-Hinshelwood mechanism, which has been conventionally considered, but also with the water-induced mechanism, which is unlikely to occur on pure Pd and Pd-based alloy catalysts; these results are supported by DFT calculations. These results reveal that the ReaxFF-MD method provides significant information for predicting the catalytic properties of NPs, which could be difficult to provide with DFT calculations; thus, it can be a useful framework for the design of nanocatalysts through complementation with a DFT method.

Nitramine compounds are typical high-energy-density materials (HEDMs) and are widely used as explosives because of their superior explosive performance over conventional energetic materials. In this work, the thermal properties of 1-nitropiperidine (NPIP), 1,4-dinitropiperazine (DNP), and 1,3,5-trinitro-1,3,5-triazinane (RDX) were investigated from quantum mechanics (QM) and reactive force field (ReaxFF) molecular dynamics simulations. We found that the bond dissociation energy of the N-NO2 bond, heat of formation, released energy, produced fragments, and oxygen balance are closely related to the incremental nitramine group. The nitramine group has a significant effect on the energetic performance of these nitramine compounds. In addition, the increase of the nitramine group will improve thermal decomposition activity, promote the generation of small molecules, and restrain the formation of carbon clusters. We hope that this work can shed new light on the design of energetic materials.

Effect of Fe and its oxides on steam gasification mechanism of lignin using ReaxFF molecular dynamics simulations

This work presents a dynamic and kinetic study on the dimerization of polycyclic aromatic hydrocarbon (PAH) molecules and radicals under flame conditions using reactive force field (ReaxFF) molecular dynamics (MD) simulations. The accuracy of the ReaxFF force field is evaluated through comparing with quantum chemistry (QC) calculations of the barrier heights and species concentrations of PAHs reacting with H and OH radicals. A series of homobinary collisions between PAH molecules/radicals are performed to reveal the influence of temperature, molecular size, PAH composition, and the number of radical sites on the dynamics and kinetics of PAH dimerization. Instead of directly forming the strong covalent bonds, the majority of the binary collisions between PAH radicals are bound with weak intermolecular interactions. Effects of oxygen on PAH radical dimerization are also investigated, which indicates that the oxygenated PAH radicals are less likely to contribute to soot nucleation. In addition, thetemperature, PAH characteristic, and radical site dependent collision efficiency for PAH radical-radical combinations isextracted from this study.

We explore here, through reactive force field (ReaxFF) molecular dynamics simulations, oleic acid upgrading on beta zeolite (BEA) regulated with nine silica-to-aluminium ratios (SARs) to investigate their acid-catalyzed deoxygenation and coking susceptibility. The selected computational descriptors involved conversion, hydrodeoxygenation, and decarboxylation/decarbonylation selectivity, biofuels yield, and coke deposition characteristics. Simulations were validated by 20.3 SAR available experimental data and used for the systematic study. High deoxygenation selectivity was found to be related to the structural sensitivity of BEA(100) on the upgrading mechanism. ReaxFF simulations revealed that altering the Al-substitution could greatly promote biofuel formation. Specifically, SARs towards the mid-region (SAR 47) favored gasoline production, while 31 SAR exploited diesel-like hydrocarbons. An optimum ratio of 37.4 SAR achieved maximum oleic acid conversion (69.8%), with high yields of gasoline and diesel fuels (15.6 and 20.4 wt%, respectively) and moderate coking (8.6 wt%). Density functional theory screening of metallic dopants allowed investigating of their deoxygenation and coke susceptibility, obtaining that Cu-BEA structure favored the optimal carbon and O-moiety adsorption (-3.89 and -1.8 eV, respectively). Furthermore, the economic and environmental analysis showed that Cu-dopped BEA displayed the lowest market price and global warming potential (71 USD/kg and 2.8 kg CO2-eq/kg).

Thermal decomposition of vegetable insulating oils from reactive molecular dynamics

Initiation mechanisms of enhanced pyrolysis and oxidation of JP-10 (exo-tetrahydrodicyclopentadiene) on functionalized graphene sheets: Insights from ReaxFF molecular dynamics simulations

Atomic-level insight into the carburization process of iron-based catalysts: A ReaxFF molecular dynamics study

The first-principles methods such as density functional theory (DFT) often produce quantitative predictions for physics and chemistry of materials with explicit descriptions of electron’s behavior. We were able to provide information of electronic structures with chemical doping and metal-insulator transition of rare-earth nickelates that cannot be easily accessible with experimental characterizations. Moreover, combining with mean-field microkinetic modeling, we utilized the DFT energetics to model water gas shift reactions catalyzed by Fe3O4at steady-state and determined favorable reaction mechanism. However, the high computational costs of DFT calculations make it impossible to investigate complex chemical processes with hundreds of elementary steps with more than thousands of atoms for realistic systems. The study of molecular high energy (HE) materials using the reactive force field (ReaxFF) has contributed to understand chemically induced detonation process with nanoscale defects as well as defect-free systems. However, the reduced accuracy of the force fields canalso lead to a different conclusion compared to DFT calculations and experimental results. Machine learning force field is a promising alternative to work with comparable simulation size and speed of ReaxFF while maintaining accuracy of DFT. In this respect, we developed a neural network reactive force field (NNRF) that was iteratively parameterized with DFT calculations to solve problems of ReaxFF. We built an efficient and accurate NNRF for complex decomposition reaction of HE materials such as high energy nitramine 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX)and predicted consistent results for experimental findings. This work aims to demonstrate the approaches to clarify the reaction details of materials using the first-principles methods and machine learning force fields to guide quantitative predictions of complex chemical process.