First-principles-based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Machine learning quantitatively characterizes the deformation and destruction of explosive molecules

Although explosives have been widely used in mines, road development, old building demolishing, and munition explosions; currently, how chemical bonds between atoms break and recombine, how the molecular structure is deformed and destroyed, how the reaction product molecules are formed, and the details for this rapid change process in explosive reactions are not yet fully understood, which limits the full use of explosive energy and safer use of explosives. This paper presents a quantitative model of molecular structure deformation using machine learning algorithms as well as a qualitative model of its relationship with molecular structure destruction, based on a molecular dynamics simulation and detailed analysis of the shock-loaded ε-CL-20, providing new perspectives for explosive community research. Specifically, the quantitative model of molecular structure deformation establishes the quantitative relationship between the molecular volume change and molecular position change, and between molecular distance change and molecular volume change using the machine learning algorithms such as Delaunay triangulation, clustering, and gradient descent. We find that the molecular spacing in explosives is strongly compressed after being shocked, and the peripheral structure can shrink inward, which is beneficial to keep the cage structure stable. When the peripheral structure is compressed to a certain extent, the cage structure volume begins to expand and is then destroyed. In addition, hydrogen atom transfer occurs within the explosive molecule. This study amplifies the structural changes and the chemical reaction process for explosive molecules after being strongly compressed by a shock wave, which can enrich the knowledge of the real detonation reaction process. The analysis method based on quantitative characterization using machine learning proposed in this study can also be used to analyze the microscopic reaction mechanism in other materials.

Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales Using Kinetic Models: Applications to Hydrocarbon Pyrolysis

We develop a method to construct temperature-dependent kinetic models of hydrocarbon pyrolysis, based on information from molecular dynamics (MD) simulations of pyrolyzing systems in the high-temperature regime. MD simulations are currently a key tool to understand the mechanism of complex chemical processes such as pyrolysis and to observe their outcomes in different conditions, but these simulations are computationally expensive and typically limited to nanoseconds of simulation time. This limitation is inconsequential at high temperatures, where equilibrium is reached quickly, but at low temperatures, the system may not equilibrate within a tractable simulation timescale. In this work, we develop a method to construct kinetic models of hydrocarbon pyrolysis using the information from the high-temperature high-reactivity regime. We then extrapolate this model to low temperatures, which enables microsecond-long simulations to be performed. We show that this approach accurately predicts the time evolution of small molecules, as well as the size and composition of long carbon chains across a wide range of temperatures and compositions. Further, we show that the range of suitable temperatures for extrapolation can easily be improved by adding more simulations to the training data. Compared to experimental results, our kinetic model leads to similar compositional trends while allowing for more detailed kinetic and mechanistic insights.

Interface dynamics and flow fields’ structure under thermal heat flux, thermal conductivity, destabilizing acceleration and inertial stabilization

Abstract

Interfaces and interfacial mixing are omnipresent in fluids, plasmas, materials in vastly different environments. A thorough understanding of their fundamentals is essential in many areas of science, mathematics, and technology. This work focuses on the classical problem of stability of a phase boundary that is a subject to fluxes of heat and mass across it for non-ideal thermally conducting fluids. We develop a rigorous theory resolving challenges not addressed before, including boundary conditions for thermal heat flux, structure of perturbation waves, and dependence of waves coupling on system parameters in a broad range of conditions. We discover the novel class of fluid instabilities in the three regimes—advection, diffusion, and low Mach—with properties that were never earlier discussed and that are defined by the interplay of the thermal heat flux, thermal conductivity and destabilizing acceleration with the inertial stabilization. We reveal the parameter controlling transitions between the regimes through varying the initial conditions. We find that the interface stability is set primarily by the macroscopic inertial mechanism balancing the destabilizing acceleration. The thermal heat flux and the microscopic thermodynamics create vortical fields in the bulk. By linking micro to macro scales, the interface is the place where balances are achieved.

Article highlights

A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis

Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable "synthesis by design" in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials.

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model

We explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

Atomic Perspective about the Reaction Mechanism and H2 Production during the Combustion of Al Nanoparticles/H2O2 Bipropellants

The combination of Al nanoparticles (ANPs) and hydrogen peroxide (H₂O₂) can serve as environmentally friendly bipropellants and maximize the energetic benefits through harnessing heat release and chemical energy stored in H₂. This work presents an atomic insight into the combustion mechanism of ANPs/H₂O₂. Two main paths, including the ANPs oxidation by H₂O₂ to produce H₂ and Al oxides, and the catalytic decomposition of H₂O₂ on ANP surface to generate O₂ and H₂O, are confirmed to maintain the combustion. OH and HOO radicals as well as H₂O, O₂, H₂, and Al oxides are detected as dominant intermediates and products therein. It is evidenced that higher temperature, smaller ANP size, and higher H₂O₂ concentration enhance the combustion. Moreover, atomic details show that the H desorption from ANPs/Al clusters is a critical step for both H₂ production and ANP oxidation. In addition, microexplosion that has been confirmed in hot and dense O₂ is not observed in H₂O₂, even with a high concentration, possibly due to a slower heat release. Besides, the observed excellent specific impulse of the ANP/H₂O₂ bipropellants could be attributed to the considerable H₂ production, instead of heat release. This work is expected to present an overall atomic perspective about the combustion mechanism of ANP/H₂O₂ bipropellants.

Comparing the performances of various density functionals for modelling the mechanisms and kinetics of bimolecular free radical reactions in aqueous solution

We systematically tested the performances of 18 density functionals for the mechanisms and kinetics of reactions of the α-hydroxyisopropyl radical with 9 organic substrates.

Interface dynamics: Mechanisms of stabilization and destabilization and structure of flow fields

Interfacial mixing and transport are nonequilibrium processes coupling kinetic to macroscopic scales. They occur in fluids, plasmas, and materials over celestial events to atoms. Grasping their fundamentals can advance a broad range of disciplines in science, mathematics, and engineering. This paper focuses on the long-standing classic problem of stability of a phase boundary-a fluid interface that has a mass flow across it. We briefly review the recent advances in theoretical and experimental studies, develop the general theoretical framework directly linking the microscopic interfacial transport to the macroscopic flow fields, discover mechanisms of interface stabilization and destabilization that have not been discussed before for both inertial and accelerated dynamics, and chart perspectives for future research.