Full-dimensional neural network potential energy surface and dynamics of the CH<sub>2</sub>OO + H<sub>2</sub>O reaction

Wu, Hao; Fu, Yanlin; Dong, Wenrui; Fu, Bina; Zhang, Dong H.

doi:10.1039/d3ra02069j

Cited by 5 publications

(10 citation statements)

References 55 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[29][30][31]34 Recently, we reported the first full-dimensional potential energy surface (PES) of the CH 2 OO + H 2 O reaction, covering the reactants to HMHP, which extends to various end products, including the hydroxymethoxy radical (HOCH 2 O, HMO) + OH, formic acid (HCOOH) + H 2 O, and formaldehyde (CH 2 O) + H 2 O 2 . 35 The rate coefficients calculated with transition state theory agree well with the experimental results. The quasiclassical Roaming, an unconventional yet intriguing mechanism, promises to yield unexpected products with final distributions remarkably distinct from those generated by the traditional MEP.…”

Section: ■ Introductionsupporting

confidence: 72%

“…The full-dimensional PES used in this study was a modified PES for the CH 2 OO + H 2 O reaction we reported recently. 35 Figure 1 shows the schematic diagrams of the PES, illustrating the configurations and energies of stationary points. Figure 1a corresponds to the collision of CH 2 OO and H 2 O, which gives rise to the formation of HMHP.…”

Section: ■ Methodsmentioning

confidence: 99%

“…As detailed in our previous work, 35 the CCSD(T)-F12 method has problems in describing the two radical OHCH 2 O + OH channels. We employed the approach of mixing as incorporated in Gaussian program, 56 which mixes some amount of the highest occupied molecular orbital (HOMO) with the lowest unoccupied molecular orbital (LUMO) of the same spin.…”

Section: ■ Methodsmentioning

confidence: 99%

“…The FIs are calculated by King's algorithm 58 using the homemade code of our group. As we did before, 35 in the ASY region, we do not consider atomic permutative symmetry between H 2 O and CH 2 OO and obtain a total of 143 FIs. Similarly, the atomic permutative symmetry between CH 2 O and H 2 O 2 in this product region is not considered, resulting in the same number of FIs as in the ASY region.…”

Section: ■ Methodsmentioning

confidence: 99%

“…The end products of the reaction between CH 2 OO and water remain highly controversial both experimentally and theoretically. Previous theoretical studies focused on the single-point energy calculations using different levels of ab initio methods to study the CH 2 OO + H 2 O reaction and the decomposition of HMHP. − , Recently, we reported the first full-dimensional potential energy surface (PES) of the CH 2 OO + H 2 O reaction, covering the reactants to HMHP, which extends to various end products, including the hydroxymethoxy radical (HOCH 2 O, HMO) + OH, formic acid (HCOOH) + H 2 O, and formaldehyde (CH 2 O) + H 2 O 2 . The rate coefficients calculated with transition state theory agree well with the experimental results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

Wu,

Fu,

et al. 2023

J. Phys. Chem. A

Self Cite

View full text Add to dashboard Cite

The CH2OO + H2O reaction is an important atmospheric process that leads to the formation of formic acid (HCOOH) and water via the intermediate hydroxymethyl hydroperoxide (HOCH2OOH, HMHP). We investigated the intricacies of this process by employing quasiclassical trajectory calculations on an accurate, full-dimensional ab initio potential energy surface (PES). In addition to the direct mechanism via the transition state (TS), an interesting roaming mechanism was found to play the predominant role in producing H2O and HCOOH. This roaming pathway is featured as the near direct dissociation of HMHP into OH and hydroxymethoxy radical, followed by the retraction of OH and abstraction of the H atom, culminating in the formation of H2O. Due to the longer interaction time of the roaming mechanism, less product translational energy was released, but more internal energies of HCOOH were obtained, as compared with the direct TS mechanism. The enhanced yield of H2O and formic acid achieved through roaming dynamics underscores the significance of dynamics simulations based on an accurate full-dimensional PES. This work provides new insights into the dynamics of the CH2OO + H2O reaction and its implications for atmospheric chemistry.

show abstract

Section: ■ Introductionsupporting

confidence: 72%

Section: ■ Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

Wu,

Fu,

et al. 2023

J. Phys. Chem. A

Self Cite

View full text Add to dashboard Cite

show abstract

An overview about neural networks potentials in molecular dynamics simulation

Martin‐Barrios,

Navas‐Conyedo,

Zhang

et al. 2024

Int J of Quantum Chemistry

View full text Add to dashboard Cite

Ab‐initio molecular dynamics (AIMD) is a key method for realistic simulation of complex atomistic systems and processes in nanoscale. In AIMD, finite‐temperature dynamical trajectories are generated by using forces computed from electronic structure calculations. In systems with high numbers of components a typical AIMD run is computationally demanding. On the other hand, machine learning (ML) is a subfield of the artificial intelligence that consist in a set of algorithms that show learning by experience with the use of input and output data where algorithms are capable of analysing and predicting the future. At present, the main application of ML techniques in atomic simulations is the development of new interatomic potentials to correctly describe the potential energy surfaces (PES). This technique is in constant progress since its inception around 30 years ago. The ML potentials combine the advantages of classical and Ab‐initio methods, that is, the efficiency of a simple functional form and the accuracy of first principles calculations. In this article we review the evolution of four generations of machine learning potentials and some of their most notable applications. This review focuses on MLPs based on neural networks. Also, we present a state of art of this topic and future trends. Finally, we report the results of a scientometric study (covering the period 1995–2023) about the impact of ML techniques applied to atomistic simulations, distribution of publications by geographical regions and hot topics investigated in the literature.

show abstract

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Song,

2024

J. Phys. Chem. A

View full text Add to dashboard Cite

The hydrogen abstraction reaction of OH + CH 3 OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/ AVTZ. Besides, the spin−orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.

show abstract

Full-dimensional neural network potential energy surface and dynamics of the CH₂OO + H₂O reaction

Abstract: A first dynamical study based on an accurate full-dimensional neural network PES for the CH2OO + H2O reaction.

Cited by 5 publications

References 55 publications

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

An overview about neural networks potentials in molecular dynamics simulation

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Contact Info

Product

Resources

About

Full-dimensional neural network potential energy surface and dynamics of the CH2OO + H2O reaction

Abstract: A first dynamical study based on an accurate full-dimensional neural network PES for the CH2OO + H2O reaction.

Cited by 5 publications

References 55 publications

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH2OO + H2O Reaction

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH2OO + H2O Reaction

An overview about neural networks potentials in molecular dynamics simulation

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces

Contact Info

Product

Resources

About

Full-dimensional neural network potential energy surface and dynamics of the CH₂OO + H₂O reaction

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

Roaming Dynamics in Hydroxymethyl Hydroperoxide Decomposition Revealed by the Full-Dimensional Potential Energy Surface of the CH₂OO + H₂O Reaction

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces