An accurate potential energy surface and ring polymer molecular dynamics study of the Cl + CH<sub>4</sub>→ HCl + CH<sub>3</sub>reaction

Liu, Yang; Li, Jun

doi:10.1039/c9cp05693a

Cited by 27 publications

(44 citation statements)

References 132 publications

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“…The MOLPRO 2019 software package is used in this work for all electronic structure calculations. , The geometries, electronic energies, and harmonic frequencies of all stationary points along each channel, including the reactants, products, and transition states (TSs), are determined at the UCCSD(T)-F12a/AVXZ (X = D, T) levels, which can efficiently provide results of an accuracy close to those calculated at the traditional UCCSD(T)/AV5Z level. , As a compromise between efficiency and computational cost, UCCSD(T)-F12a/AVTZ is selected to calculate the electronic energies of the sampled points for fitting. Indeed, this method has been used successfully to develop reliable PESs for various reactions in an efficient way. ,,,− The T 1 diagnostic values of all stationary points are smaller than 0.033, as shown in Table , indicating that the single-reference based UCCSD(T)-F12a/AVTZ is appropriate for the title reaction. In addition, for the sampled points, we discarded those points ( ca .…”

Section: Methodsmentioning

confidence: 99%

“…The selected ab initio points are then fitted by the PIP-NN − and HD-NN methods. − The details of the PIP-NN method can be found in our recent applications, ,,,,, and the Supporting Information. Briefly, the permutation invariance property is implemented with permutation invariant polynomials (PIPs) , of the geometries as the input layer of a single NN.…”

Section: Methodsmentioning

confidence: 99%

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Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

Lü

Behler

2020

J. Phys. Chem. A

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The H + CH 3 OH reaction, which plays an important role in combustion and the interstellar medium, presents a prototypical system with multi channels and tight transition states. However, no globally reliable potential energy surface (PES) has been available to date. Here we develop global analytical PESs for this system using the permutation-invariant polynomial neural network (PIP-NN) and the high-dimensional neural network (HD-NN) methods based on a large number of data points calculated at the level of the explicitly correlated unrestricted coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-ζ basis set (UCCSD(T)-F12a/AVTZ). We demonstrate that both machine learning PESs are able to accurately describe all dynamically relevant reaction channels. At a collision energy of 20 kcal/mol, quasi-classical trajectory calculations reveal that the dominant channel is the hydrogen abstraction from the methyl site, yielding H 2 + CH 2 OH. The reaction of this major channel takes place mainly via the direct rebound mechanism. Both the vibrational and rotational states of the H 2 product are relatively cold, and large portions of the available energy are converted into the product translational motion.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

Lü

Behler

2020

J. Phys. Chem. A

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“…2500 points were selected and calculated at the same electronic structure level, 3-state-MRCI+Q-F12/CVDZ-F12. Then all these data points were used to fit the lowest triplet state PES of the title reaction by using the PIP-NN nonlinear fitting method. − Detailed information about the PIP-NN fitting and relevant discussion can be found in other PIP-NN PESs for various reactions, including HOSO, OH + HO 2 , , OH + H 2 O, H/Cl/OH + CH 4 , − F/Cl/H + CH 3 OH, − N 2 O + C 2 H 2 , and so on. Briefly, PIP-NN combines both advantages of PIP , and traditional NN fitting: the fitting performance is guaranteed by the NN and the rigorous permutation invariant property; namely, the potential energy is invariant with respect to exchanges among identical atoms which is incorporated by the input layer of NN, PIPs.…”

Section: Theorymentioning

confidence: 99%

A Full-Dimensional Potential Energy Surface and Dynamics of the Multichannel Reaction between H and HO₂

2021

J. Phys. Chem. A

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In addition to its vital significance in combustion and atmospheric chemistry, the reaction between H′ and HO 2 on the ground triplet state represents a prototype with multiple product channels, including H 2 + O 2 , OH + OH, O + H 2 O, and H + H′O 2 . In this work, a full-dimensional accurate potential energy surface (PES) for the title reaction was developed to provide reliable descriptions for all dynamically relevant regions. Using this PES, we adopted the quasi-classical trajectory approach to study the corresponding reaction dynamics, including the reactivity of each product channel and the associated product branching ratio, the product energy distributions, product angular distributions, and associated microscopic mechanisms. For representing distributions of the product energies, such as product translational energy as well as product rotational and vibrational energies, both the traditional histogram and the kernel density estimation (KDE) methods were used and compared. It seems that the features of the resulting distributions in this work are very similar to each other among different methods. The KDE method is suggested for statistics, particularly for those populations with small oscillations in the histogram plot.Article pubs.acs.org/JPCA

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“…This extension is called MB-PIP-NN. The nonlinear nature of NNs provides a flexible means to approximate real-valued multi-dimensional functions accurately, and the PIP-NN and MB-PIP-NN methods have several strong points, including potentially higher fidelity than the linear least-squares permutationally invariant polynomial (PIP) method ,,, and greater efficiency than the interpolating moving least-squares (IMLS) method. , The PIP-NN method has recently been employed successfully to fit high-fidelity PESs for polyatomic reactive systems, such as OH + H 2 O, OH + HO 2 , , H/Cl/OH + CH 4 , − and F + CH 3 OH. − …”

Section: Introductionmentioning

confidence: 99%

Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N₄

Varga

Truhlar

et al. 2020

J. Chem. Theory Comput.

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A potential energy surface (PES) for high-energy collisions between nitrogen molecules is useful for modeling chemical dynamics in shock waves and plasmas. In the present work, we fit the many-body (MB) component of the ground-state PES of N 4 to an analytic function using neural networks (NNs) with permutationally invariant polynomials (PIPs). The fitting dataset of the N 4 system is an extension of one used previously, extended with 4859 new CASPT2 points and 13 new CCSD(T) points to reach a total of 21 406 points. The MB-PIP-NN fit required a very complete coverage of the geometry domain in order to get a physical fit, and we devised several tactical steps to achieve this, including trajectory calculations, the comparison of the NN fit with PIP fits with mixed-exponential Gaussian bond order variables, and searching geometry regions with sparse data coverage. With these efforts, the final dataset is more suitable for a NN fit. The energy range of the dataset is much wider than those used in other systems previously fitted by NNs, and there are more rugged surface regions than usual due to locally avoided crossings. The performance of the new MB-PIP-NN fit is compared to that of another new fit to the same data by least-squares methods not employing NNs, and the mean unsigned deviation from the electronic structure calculations is reduced by a factor of 3, although the computing time to calculate a force for a trajectory calculation is larger by an order of magnitude. The accuracy achievable with the NN fit for this difficult system is very impressive, and we anticipate that the NN method can give useful fits to difficult cases that cannot be achieved by more conventional methods.

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An accurate potential energy surface and ring polymer molecular dynamics study of the Cl + CH₄→ HCl + CH₃reaction

Abstract: Thermal rate coefficients for the Cl + CH4/CD4 reactions were studied on a new full-dimensional accurate potential energy surface with the spin–orbit corrections considered in the entrance channel.

Cited by 27 publications

References 132 publications

Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

A Full-Dimensional Potential Energy Surface and Dynamics of the Multichannel Reaction between H and HO₂

Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N₄

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An accurate potential energy surface and ring polymer molecular dynamics study of the Cl + CH4→ HCl + CH3reaction

Abstract: Thermal rate coefficients for the Cl + CH4/CD4 reactions were studied on a new full-dimensional accurate potential energy surface with the spin–orbit corrections considered in the entrance channel.

Cited by 27 publications

References 132 publications

Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance

Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance

A Full-Dimensional Potential Energy Surface and Dynamics of the Multichannel Reaction between H and HO2

Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N4

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An accurate potential energy surface and ring polymer molecular dynamics study of the Cl + CH₄→ HCl + CH₃reaction

Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

Accurate Global Potential Energy Surfaces for the H + CH₃OH Reaction by Neural Network Fitting with Permutation Invariance

A Full-Dimensional Potential Energy Surface and Dynamics of the Multichannel Reaction between H and HO₂

Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N₄