Figure S1: Time evolution of main species for 1800K and 3500K at 0.7V0 Scheme S1: Hydrogen transfer routes between TNT and TNT-H and the cleavage of C-NO 2 from TNT molecule missing a ring bound hydrogen. Units: kcal/mol. S4 Scheme S2: Reactions of TNT radical missing a NO 2 group (TNT-NO 2). Units: kcal/mol. S5 Scheme S3. Total energies for ortho hydrogen transfer (top part) and further decomposition routes (bottom part). Units: kcal/mol.
The reaction kinetics of the thermal decomposition of hot, dense liquid TNT was studied from first-principles-based ReaxFF multiscale reactive dynamics simulation strategy. The decomposition process was followed starting from the initial liquid phase, decomposition to radicals, continuing through formation of carbon-clusters products, and finally to formation of the stable gaseous products. The activation energy of the initial endothermic decomposition rate and the subsequent exothermic reactions were determined as a function of density. Analysis of fragments production in different densities and temperatures is presented. We find that unimolecular C–N bond scission dominates at the lower densities (producing NO2), whereas dimer formation and decomposition to TNT derivatives and smaller gaseous fragments prevails at higher compressions. At higher densities, enhanced carbon-clustering is observed, while the initial gaseous fragments formation is suppressed. Increasing the temperature speeds up the production of both clusters and gaseous products. The activation energy for the initial decomposition stage of ambient liquid TNT is ∼36 kcal/mol, close to the measured value (∼40 kcal/mol). This value is ∼25 kcal/mol lower than the corresponding gas phase C–N bond scission. Finally, we suggest a simple linear growth kinetic model for describing the clustering process, which provides very good agreement with simulation results.
Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.
Molecular dynamics (MD) simulations provide an important link between theories and experiments. While ab initio methods can be prohibitively costly, the ReaxFF force field has facilitated in silico studies of chemical reactivity in complex, condensed-phase systems. However, the relatively poor energy conservation in ReaxFF MD has either limited the applicability to short time scales, in cases where energy propagation is important, or has required a continuous coupling of the system to a heat bath. In this study, we reveal the root cause of the unsatisfactory energy conservation, and offer a straightforward solution. The new scheme results in orders of magnitude improvement in energy conservation, numerical stability, and accuracy of ReaxFF force fields, compared to the previous state-of-the-art, at no additional cost. We anticipate that these improvements will open new avenues of research for more accurate reactive simulations in complex systems on long time scales.
The development of a reactive force field (ReaxFF formalism) for hydrazoic acid (HN 3 ), a highly sensitive liquid energetic material, is reported. The force field accurately reproduces results of density functional theory (DFT) calculations. The quality and performance of the force field are examined by detailed comparison with DFT calculations related to uni, bi, and trimolecular thermal decomposition routes. Reactive molecular dynamics (RMD) simulations are performed to reveal the initial chemical events governing the detonation chemistry of liquid HN 3 . The outcome of these simulations compares very well with recent results of tight-binding DFT molecular dynamics and thermodynamic calculations. On the basis of our RMD simulations, predictions were made for the activation energies and volumes in a broad range of temperatures and initial material compressions.
Despite decades of research, how life began on Earth remains one of the most challenging scientific conundrums facing modern science. It is agreed that the first step was synthesis of organic compounds essential to obtain amino acids and their polymers. Several possible scenarios that could accomplish this step, using simple inorganic molecules, have been suggested and studied over the years. The present study examines, using atomistic reactive molecular dynamics simulations, the long-standing suggestion that natural cavitation in primordial oceans was a dominant mechanism of organic molecule synthesis. The simulations allow, for the first time, direct observation of the rich and complex sonochemistry occurring inside a collapsing bubble filled with water and dissolved gases of the early atmosphere. The simulation results suggest that dissolved CH4 is the most efficient carbon source to produce amino acids, while CO and CO2 lead to amino acid synthesis with lower yields. The efficiency of amino acid synthesis also depends on the nitrogen source used (i.e., N2, NH3) and on the presence of HCN. Moreover, cavitation may have contributed to the increase in concentration of NH3 in primordial oceans and to the production and liberation of molecular O2 into the early atmosphere. Overall, the picture that emerges from the simulations indicates that collapsing bubbles may have served as natural bioreactors in primordial oceans, producing the basic chemical ingredients required for the beginning of life.
Pentaerythritol tetranitrate (PETN) finds many uses in the energetic materials community. Due to the recent availability of erythritol, erythritol tetranitrate (ETN) can now be readily synthesized. Accordingly, its complete characterization, especially its stability, is of great interest. This work examines the thermal decomposition of ETN, both through experimental and computational methods. In addition to kinetic parameters, decomposition products were examined to elucidate its decomposition pathway. It is found that ETN begins its decomposition sequence by a unimolecular homolytic cleavage of the internal and external O−NO 2 bonds, while the competing HONO elimination reaction is largely suppressed. The global activation energy for decomposition is found to be 104.3 kJ/mol with a pre-exponential factor of 3.72 × 10 9 s −1 . Despite the ability to exist in a molten state, ETN has a lower thermal stability than its counterpart PETN.
Diffusion describes the stochastic motion of particles and is often a key factor in determining the functionality of materials. Modeling diffusion of atoms can be very challenging for heterogeneous systems with high energy barriers. In this report, popular computational methodologies are covered to study diffusion mechanisms that are widely used in the community and both their strengths and weaknesses are presented. In static approaches, such as electronic structure theory, diffusion mechanisms are usually analyzed within the nudged elastic band (NEB) framework on the ground electronic surface usually obtained from a density functional theory (DFT) calculation. Another common approach to study diffusion mechanisms is based on molecular dynamics (MD) where the equations of motion are solved for every time step for all the atoms in the system. Unfortunately, both the static and dynamic approaches have inherent limitations that restrict the classes of diffusive systems that can be efficiently treated. Such limitations could be remedied by exploiting recent advances in artificial intelligence and machine learning techniques. Here, the most promising approaches in this emerging field for modeling diffusion are reported. It is believed that these knowledge-intensive methods have a bright future ahead for the study of diffusion mechanisms in advanced functional materials.observed in liquids, gases, and solid-state materials. One of the main cornerstones for the macroscopic understanding of diffusion was laid down in the mid-19th century by Adolf Fick. By observing Graham's experiments in gases and salt water, and by the analogy between Fourier's law of thermal conduction and diffusion, Fick developed two equations known as the Fick's laws , , , J D C x y z t
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