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Abstract

Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of both accurate and efficient PES has attracted significant effort in the past 2 decades. A recently developed deep potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training data set. In this work, a data set construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimizing the redundancy of the data set. This greatly reduces the cost of computational resources required for ab initio calculations. Based on this method, we constructed a data set for the pyrolysis of n-dodecane, which contains 35 496 structures. The reactive MD simulation with the DP model trained based on this data set revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this data set shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training data sets for similar systems.

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... Machine learning potentials offer a potential mechanism to improve the accuracy and efficiency of QM/MM simulations, and they have had considerable impact in the development of methods to study chemical reactions. [15][16][17][18][19][20][21] Herein we develop an approach whereby we employ a recently described deep-potential range correction (DPRc) model 22 to enhance the accuracy of a fast, approximate base QM/MM model to reproduce the energies and forces of a much more computationally costly target QM/MM model. The new model parametrizes the DPRc potential using a machine learning neural network training procedure 22 to correct the 2nd-order density-functional tight-binding (DFTB2) semiempirical method [23][24][25] to reproduce the PBE0/6-31G* energies and forces in explicit solvent QM/MM calculations. ...
... Whereas the mean absolute error (MAE) for activation and reaction free energies for DFTB2 with respect to PBE0/6-31G* reference values are 3.4 and 9.4 kcal/mol, re- · · · · · · · · · · · · PBE0/6-31G* -1.62 -0. 21 ...
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High-temperature, reactive gas flow is inherently nonequilibrium in terms of energy and state population distributions. Modeling such conditions is challenging even for the smallest molecular systems due to the extremely large number of accessible states and transitions between them. Here, neural networks (NNs) trained on explicitly simulated data are constructed and shown to provide quantitatively realistic descriptions which can be used in mesoscale simulation approaches such as Direct Simulation Monte Carlo to model gas flow at the hypersonic regime. As an example, the state-to-state cross sections for N(⁴S) + NO(²Π) → O(³P) + N2(X1Σg+) are computed from quasiclassical trajectory (QCT) simulations. By training NNs on a sparsely sampled noisy set of state-to-state cross sections, it is demonstrated that independently generated reference data are predicted with high accuracy. State-specific and total reaction rates as a function of temperature from the NN are in quantitative agreement with explicit QCT simulations and confirm earlier simulations, and the final state distributions of the vibrational and rotational energies agree as well. Thus, NNs trained on physical reference data can provide a viable alternative to computationally demanding explicit evaluation of the microscopic information at run time. This will considerably advance the ability to realistically model nonequilibrium ensembles for network-based simulations.
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In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multi-state potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH2NH. After including geometries near conical intersection in the training set, the DNN models accurately reproduce excited-state topological structures, photoisomerization paths, and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules.
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The C + NO collision system is of interest in the area of high-temperature combustion and atmospheric chemistry. In this work, full dimensional potential energy surfaces for the ²A′, ²A″, and ⁴A″ electronic states of the [CNO] system have been constructed following a reproducing kernel Hilbert space approach. For this purpose, more than 50 000 ab initio energies are calculated at the MRCI+Q/aug-cc-pVTZ level of theory. The dynamical simulations for the C(³P) + NO(X²Π) → O(³P) + CN(X²Σ⁺), N(²D)/N(⁴S) + CO(X¹Σ⁺) reactive collisions are carried out on the newly generated surfaces using the quasiclassical trajectory (QCT) calculation method to obtain reaction probabilities, rate coefficients, and the distribution of product states. Preliminary quantum calculations are also carried out on the surfaces to obtain the reaction probabilities and compared with QCT results. The effect of nonadiabatic transitions on the dynamics for this title reaction is explored within the Landau-Zener framework. QCT simulations have been performed to simulate molecular beam experiment for the title reaction at 0.06 and 0.23 eV of relative collision energies. Results obtained from theoretical calculations are in good agreement with the available experimental as well as theoretical data reported in the literature. Finally, the reaction is studied at temperatures that are not practically achievable in the laboratory environment to provide insight into the reaction dynamics at temperatures relevant to hypersonic flight.
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The current status of reactive molecular dynamics (MD) simulations is summarized. Both, methodological aspects and applications to problems ranging from gas phase reaction dynamics to ligand‐binding in solvated proteins are discussed, focusing on extracting information from simulations that cannot easily be obtained from experiments alone. One specific example is the structural interpretation of the ligand rebinding time scales extracted from state‐of‐the art time‐resolved experiments. Atomistic simulations employing validated reactive interaction potentials are capable of providing structural information about the time scales involved. Both, merits and shortcomings of the various methods are discussed and the outlook summarizes possible future avenues such as reactive potentials based on machine learning techniques. This article is categorized under: • Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods • Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics • Molecular and Statistical Mechanics > Molecular Interactions
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n-Dodecane combustion was investigated experimentally and numerically in present study. Pyrolysis experiments of n-dodecane at pressures of 0.0066, 0.039, 0.197 and 1 atm, temperatures from 750 to 1430 K were studied in a flow reactor. Mole fractions of n-dodecane, argon and pyrolysis products (including active radicals) were evaluated. A kinetic model of n-dodecane was developed by validating both present and literature reported experiments. The rate of production analysis reveals H-abstraction and C–C bond fission reactions are main consumption pathways of n-dodecane. The β-C–C scission reactions of alkyls contribute to the formation of alkenes, which are mainly consumed via the allylic C–C fission reactions. As a soot precursor, benzene is largely produced from the recombination of C3 species. Moreover, effects of carbon chain length on flow reactor pyrolysis were investigated for n-decane, n-dodecane and n-tetradecane. The decay of n-tetradecane is the fastest, followed by n-dodecane and n-decane, indicating that the pyrolysis reactivity of n-alkanes increases as the carbon chain length increases from C10 to C14 n-alkanes. Ignition delay times and laminar burning velocities (LBVs) of n-alkanes under similar conditions were also compared, the result shows that effects of the carbon chain length on ignition delay times and LBVs are slight.
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Despite the ever-increasing computer power, accurate ab initio calculations for large systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical energy functions are used. Most current approaches are either transferable between different chemical systems, but not particularly accurate, or they are fine-tuned to a specific application. In this work, a data-driven method to construct a potential energy surface based on neural networks is presented. Since the total energy is decomposed into local atomic contributions, the evaluation is easily parallelizable and scales linearly with system size. With prediction errors below 0.5 kcal mol⁻¹ for both unknown molecules and configurations, the method is accurate across chemical and configurational space, which is demonstrated by applying it to datasets from nonreactive and reactive molecular dynamics simulations and a diverse database of equilibrium structures. The possibility to use small molecules as reference data to predict larger structures is also explored. Since the descriptor only uses local information, high-level ab initio methods, which are computationally too expensive for large molecules, become feasible for generating the necessary reference data used to train the neural network.
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Reactive force field (Reaxff) is a powerful method, which employs bond order/bond length formulism in order to describe the bond breaking and bond re-formation. In this work, a modification to the bond order formula was made and Reaxff parameters were re-optimized. The underlying idea of these modifications is to improve the energy gradient. Better agreements of the bond dissociation potential curves with the QM curves were obtained based on the aforementioned changes. Reaxff simulation was carried out to gain the understandings of the iso-octane pyrolysis. The new parameters based apparent rate constants of the iso-octane decomposition fit well with the experimental results. The simulation results are in agreement with the existing experimental results. A maximum of C2-hydrocarbons were found to have the largest percentage. And distribution of the iso-octane decomposition pathway was illustrated.
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A detailed insight of key reactive events related to oxidation and pyrolysis of hydrocarbon fuels further enhances our understanding of combustion chemistry. Though comprehensive kinetic models are available for smaller hydrocarbons (typically C3 or lower), developing and validating reaction mechanisms for larger hydrocarbons is a daunting task, due to the complexity of their reaction networks. The ReaxFF method provides an attractive computational method to obtain reaction kinetics for complex fuel and fuel mixtures, providing an accuracy approaching ab-initio based methods, but with a significantly lower computational expense. The development of the first ReaxFF combustion force field by Chenoweth et al. (CHO-2008 parameter set) in 2008 has opened new avenues for researchers to investigate combustion chemistry from atomistic-level. In this manuscript, we seek to address two issues with the CHO-2008 ReaxFF description. While the CHO-2008 description has achieved significant popularity for studying large hydrocarbon combustion, it fails to accurately describe the chemistry of small hydrocarbon oxidation, especially conversion of CO2 from CO, which is highly relevant to syngas combustion. Additionally, CHO-2008 description obtained faster than expected H-abstraction by O2 from hydrocarbons, thus underestimating the oxidation initiation temperature. In this study, we seek to systemically improve CHO-2008 description and validate it for these cases. Additionally, our aim was to retain the accuracy of the 2008 description for larger hydrocarbons and provide similar quality results. Thus, we expanded the ReaxFF CHO-2008 DFT-based training set by including reactions and transition state structures relevant to the syngas and oxidation initiation pathways and re-trained the parameters. To validate the quality of our force field, we performed high temperature NVT-MD simulations to study oxidation and pyrolysis of four different hydrocarbon fuels, namely syngas, methane, JP-10 and n-butylbenzene. Results obtained from syngas and methane oxidation simulation indicated that our re-developed parameters (named as CHO-2016 parameter set) has significantly improved the C1 chemistry predicted by ReaxFF and has solved the low temperature oxidation initiation problem. Moreover, Arrhenius parameters of JP-10 decomposition and initiation mechanism pathways of n-butylbenzene pyrolysis obtained using CHO-2016 parameter set are also in good agreement with both experimental and CHO-2008 simulation results. This demonstrated the transferability of CHO-2016 description for a wide range of hydrocarbon chemistry.
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A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH3 and CH4 were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.
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Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties—namely that they are noiseless and targeted training data can be produced on-demand—that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which makes it compatible with a wide variety of commercial and open-source electronic structure codes. We finally demonstrate that the neural network model inside Amp can accurately interpolate electronic structure energies as well as forces of thousands of multi-species atomic systems.
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Initiator could accelerate the rate of hydrocarbon pyrolysis and reduce the required material temperatures for a hypersonic aircraft heat exchanger/reactor. Nitroalkanes were proposed as the effective initiator because of the lower CN bond dissociation energy. In order to investigate the initiation mechanism of nitroalkanes on hydrocarbon pyrolysis, the pyrolysis of n-decane, nitromethane and their binary mixture were carried out at 30, 150 and 760 Torr in a flow reactor with synchrotron vacuum ultraviolet photoionization mass spectrometry (SVUV-PIMS). The identified and quantified pyrolysis species include C1C2 alkanes, C2C10 alkenes, C3C6 dialkenes, C2C3 alkynes, nitrogen oxides such as NO and NO2, benzene, and radicals including CH3, C3H3, and C3H5, which shed light on the mechanism of n-decane and nitromethane pyrolysis, as well as the interactions of these two fuels. The experimental results indicate that the addition of nitromethane decreases the initial decomposition temperature of n-decane, and a stronger promotion effect could be obtained as the experimental pressure increases. The distributions of alkanes, alkenes, dialkenes, alkynes and benzene are also influenced by the addition of nitromethane. A detailed kinetic model with 266 species and 1648 reactions was developed and validated against the mole fraction profiles of reactants, major products and important intermediates during the pyrolysis of each fuel and their binary mixture. The satisfactory model prediction to the experimental measurements permits the analysis of the kinetic effect of nitromethane initiation on the pyrolysis of n-decane. So that, the increase of the conversion rate at a lower temperature, the selectivity of decomposition products, and reduction of benzene formation are better understood.
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The current study investigates n-dodecane (n-C12H26) pyrolysis and oxidation kinetics in the temperature regime of 1000-1300 K in the Stanford Variable Pressure Flow Reactor facility. The reactor environment is vitiated and the experiments were conducted at atmospheric pressure. Species time history data were collected for n-dodecane and oxygen, as well as for 12 intermediate and product species over a span of 1-40 ms residence times using real time gas chromatography. The experimental data were compared against the predictions of four detailed kinetic models. The results showed that the fuel oxidation proceeds through an early pyrolytic stage where the fuel breaks down into smaller hydrocarbon fragments, including mostly C2-4 alkenes, and a late oxidation stage where the fragments oxidize to CO. The kinetic models were observed to diverge notably in their predictions from one another. Sensitivity and flux analysis identified the cause of the divergences to differences in the small hydrocarbon chemistry modeling. Finally, the flow reactor data were used to demonstrate how model uncertainty minimization can improve model predictions. It is shown that after uncertainty minimization against a selected set of n-dodecane combustion data, the predictions of the resulting optimized model are improved notably for all existing n-dodecane data sets tested, including those of the current flow reactor study that were not part of the optimization target list.
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A short overview is presented of the density functional theory and molecular dynamics (DFT–MD) method and of a code (CPMD) based on a plane wave scheme. Its power is shown through the survey of specific applications to diverse frontier areas of chemistry and materials science that make use of parallel computing.
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We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian approximation potentials (GAP) framework, discuss a variety of descriptors, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for noncommercial use. © 2015 Wiley Periodicals, Inc.
Article
To make practical the molecular dynamics simulation of large scale reactive chemical systems (1000s of atoms), we developed ReaxFF, a force field for reactive systems. ReaxFF uses a general relationship between bond distance and bond order on one hand and between bond order and bond energy on the other hand that leads to proper dissociation of bonds to separated atoms. Other valence terms present in the force field (angle and torsion) are defined in terms of the same bond orders so that all these terms go to zero smoothly as bonds break. In addition, ReaxFF has Coulomb and Morse (van der Waals) potentials to describe nonbond interactions between all atoms (no exclusions). These nonbond interactions are shielded at short range so that the Coulomb and van der Waals interactions become constant as R-ij --> 0. We report here the ReaxFF for hydrocarbons. The parameters were derived from quantum chemical calculations on bond dissociation and reactions of small molecules plus heat of formation and geometry data for a number of stable hydrocarbon compounds. We find that the ReaxFF provides a good description of these data. Generally, the results are of an accuracy similar or better than PM3, while ReaxFF is about 100 times faster. In turn, the PM3 is about 100 times faster than the QC calculations. Thus, with ReaxFF we hope to be able to study complex reactions in hydrocarbons.
Article
cp2k has become a versatile open-source tool for the simulation of complex systems on the nanometer scale. It allows for sampling and exploring potential energy surfaces that can be computed using a variety of empirical and first principles models. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern and massively parallel hardware. This review briefly summarizes the main capabilities and illustrates with recent applications the science cp2k has enabled in the field of atomistic simulation. WIREs Comput Mol Sci 2014, 4:15–25. doi: 10.1002/wcms.1159 The authors have declared no conflicts of interest in relation to this article. For further resources related to this article, please visit the WIREs website.
Article
High pressure n-decane and n-dodecane shock tube experiments were conducted to assist in the development of a Jet A surrogate kinetic model. Jet A is a kerosene based jet fuel composed of hundreds of hydrocarbons consisting of paraffins, olefins, aromatics and naphthenes. In the formulation of the surrogate mixture, n-decane or n-dodecane represent the normal paraffin class of hydrocarbons present in aviation fuels like Jet A. The experimental work on both n-alkanes was performed in a heated high pressure single pulse shock tube. The mole fractions of the stable species were determined using gas chromatography and mass spectroscopy. Experimental data on both n-decane and n-dodecane oxidation and pyrolysis were obtained for temperatures from 867 to 1739 K, pressures from 19 to 74 atm, reaction times from 1.15 to 3.47 ms, and equivalence ratios from 0.46 to 2.05, and ∞. Both n-decane and n-dodecane oxidation showed that the fuel decays through thermally driven oxygen free decomposition at the conditions studied. This observation prompted an experimental and modeling study of n-decane and n-dodecane pyrolysis using a recently submitted revised n-decane/iso-octane/toluene surrogate model. The surrogate model was extended to n-dodecane in order to facilitate the study of the species and the 1-olefin species quantified during the pyrolysis of n-dodecane and n-decane were revised with additional reactions and reaction rate constants modified with rate constants taken from literature. When compared against a recently published generalized n-alkane model and the original and revised surrogate models, the revised (based on our experimental work) and extended surrogate model showed improvements in predicting 1-olefin species profiles from pyrolytic and oxidative n-decane and n-dodecane experiments. The revised and extended model when compared to the published generalized n-alkane and surrogate models also showed improvements in predicting species profiles from flow reactor n-decane oxidation experiments, but similarly predicted n-decane and n-dodecane ignition delay times.
Article
A simple, general, and rigorous scheme for adapting permutation symmetry in molecular systems is proposed and tested for fitting global potential energy surfaces using neural networks (NNs). The symmetry adaptation is realized by using low-order permutation invariant polynomials (PIPs) as inputs for the NNs. This so-called PIP-NN approach is applied to the H + H2 and Cl + H2 systems and the analytical potential energy surfaces for these two systems were accurately reproduced by PIP-NN. The accuracy of the NN potential energy surfaces was confirmed by quantum scattering calculations.
Article
For pyrolysis of n-tetradecane at 450 C under elevated pressures (about 2--9 MPa) for 6--480 min, the major products in the early stage are n-alkanes in the carbon number range of C[sub 1]-C[sub 11] and 1-alkenes in the carbon number range of C[sub 2]-C[sub 14]. Formation of the olefinic product with 13 carbon atoms is very limited, but 1-C[sub 12]H[sub 24] is as abundant as the 1-alkenes in the range of C[sub 2]-C[sub 11]. As compared to the well-known gas-phase pyrolysis, the molar ratios of alkenes to alkanes are much smaller ([le]1), except the ratio for the group with 12 carbon atoms where the 1-C[sub 12]H[sub 24]/n-C[sub 12]H[sub 26] ratio can be as high as 9 in the early stage. This is because 1-C[sub 12]H[sub 24] is produced from [beta]-scission of a secondary 4-C[sub 14]H[sub 29] radical, whereas n-C[sub 12]H[sub 26] is formed from a primary 1-C[sub 14]H[sub 29] radical, whose population is much less than the secondary radical due to both higher activation energy and the competing isomerization reaction. There appeared a preferential formation of 1-C[sub 6]H[sub 12] and 1-C[sub 5]H[sub 10] among the 1-alkenes formed after 12 min. This may be attributed to the 1,5-shift and 1,4-shift isomerization of primary radicals formed during tetradecane pyrolysis. The peak of carbon number distribution shifts toward 2 for paraffinic products and toward 3 for olefinic products, and the ratios of alkenes to alkanes decrease with increasing residence time up to 60 min. The general reaction mechanism is characterized by the one-step decomposition of secondary radicals and the 1,5 and 1,4-shift isomerization of primary radicals to secondary radicals. The substrate alkane molecules are the source for hydrogen abstraction in the early stage, but in the later stages the olefinic products also undergo the H-abstraction reactions, which lead to the formation of cyclic alkenes/alkanes and alkylaromatics.
Article
Thermal decomposition of n-decane is studied in a flow reactor heated by high-frequency induction around 810 °C (1083 K). The main products, determined by gas-phase chromatography, are hydrogen, methane, ethylene, and C3 to C9 α-olefins. A theoretical chain radical mechanism indicates that these compounds should be primary products. We estimate the importance of intramolecular isomerizations by 1,4- and 1,5-hydrogen transfer (transition states with five and six centers, including H) with respect to other elementary processes for bond breaking. Nine theoretical and independent main primary stoichiometries can be deduced from this radical mechanism. Secondary products are also formed. The main secondary products are benzene, toluene, and 1,3-butadiene. To qualitatively interpret the formation of these products (initial rate zero), elementary processes, including the main primary products, are proposed.