A chemical dynamics study of the HCl + HCl+ reaction

Luo, Yuheng; Kreuscher, Thomas; Kang, Christopher H.; Hase, William L.; Weitzel, Karl‐Michael; Sun, Rui

doi:10.1016/j.ijms.2020.116515

Cited by 9 publications

(35 citation statements)

References 50 publications

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“…The balance between the mass and translational energy could also impact the outcome of the ion-molecular reaction as well. 15,44…”

Section: Resultsmentioning

confidence: 99%

“…The DR and DS mechanisms have been observed in previous simulations of the X À + CH 3 I (X = F, Cl) and HCl + HCl + reactions. 19,43,44 The indirect mechanism is complex-mediated, in which the reactants are usually trapped in the pre-and/or postreaction potential energy wells, forming hydrogen-bonded prereaction complexes, ion-dipole pre-reaction complexes, or postreaction complexes. 45,46 The P r (b) of these three mechanisms versus b are plotted in Fig.…”

Section: Reaction Dynamics Simulationsmentioning

confidence: 99%

“…The balance between the mass and translational energy could also impact the outcome of the ion-molecular reaction as well. 15,44 Most interestingly, for the reactions of the isoelectronic species F À 51,53,54 and OH À 10 with CH 3 Y (Y = Cl and I), the predominant atomic reaction mechanisms of the S N 2 pathway is discussed. The fractions of the different mechanisms for the X À + CH 3 Y reactions (X = F, OH; Y = Cl, I) are listed in Table 2.…”

Section: Contest Between the Leaving Group And The Nucleophile For Xmentioning

confidence: 99%

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Reaction mechanism conversion induced by the contest of nucleophile and leaving group

Zhao

Zhen

et al. 2022

Phys. Chem. Chem. Phys.

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“…The balance between the mass and translational energy could also impact the outcome of the ion-molecular reaction as well. 15,44…”

Section: Resultsmentioning

confidence: 99%

Section: Reaction Dynamics Simulationsmentioning

confidence: 99%

Section: Contest Between the Leaving Group And The Nucleophile For Xmentioning

confidence: 99%

See 1 more Smart Citation

Reaction mechanism conversion induced by the contest of nucleophile and leaving group

Zhao

Zhen

et al. 2022

Phys. Chem. Chem. Phys.

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“…The potential energy and the AIMD are both generated at the MP2/cc-pVDZ/lanl08d level of theory. [48,49] new regiments of ab initio calculations to train these specific areas in the phase space [35,36] and/or the global ML potential energy surface (PES) would have to be iteratively retrained. [41,42,43,37] To our best knowledge, there has not been a ML method that has been developed to tackle the complexity of the phase space involved in a chemical reaction.…”

Section: Introductionmentioning

confidence: 99%

“…This figure shows that although the chemical space of interest in other ML works is able to sample the isomerization of two intermediates separated by a low transition state (star), it is not nearly enough for chemical reactions involving separated molecules. The potential energy and the AIMD are both generated at the MP2/cc-pVDZ/lanl08d level of theory [48,49].…”

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confidence: 99%

Active Machine Learning for Chemical Dynamics Simulations. I. Estimating the Energy Gradient

Fujioka¹,

Sun²

2021

Preprint

Self Cite

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Ab initio molecular dymamics (AIMD) simulation studies are a direct way to visualize chemical reactions and help elucidate non-statistical dynamics that does not follow the intrinsic reaction coordinate. However, due to the enormous amount of the ab initio energy gradient calculations needed for AIMD, it has been largely restrained to limited sampling and low level of theory (i.e., density functional theory with small basis sets). To overcome this issue, a number of machine learning (ML) methods have been employed to predict the energy gradient of the system of interest. In this manuscript, we outline the theoretical foundations of a novel ML method which trains from a varying set of atomic positions and their energy gradients, called interpolating moving ridge regression (IMRR), and directly predicts the energy gradient of a new set of atomic positions. Several key theoretical findings are presented regarding the inputs used to train IMRR and the predicted energy gradient. A hyperparameter used to guide IMRR is rigorously examined as well. The method is then applied to three bimolecular reactions studied with AIMD, including HBr+ + CO2, H2S + CH, and C4H2 + CH, to demonstrate IMRR’s performance on different chemical systems of different sizes. This manuscript also compares the computational cost of the energy gradient calculation with IMRR vs. ab initio, and the results highlight IMRR as a viable option to greatly increase the efficiency of AIMD.

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Ab initio molecular dynamics benchmarking study of machine-learned potential energy surfaces for the HBr+ + HCl reaction

Fujioka¹,

Lam²,

Loi³

et al. 2023

Carbon Trends

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A chemical dynamics study of the HCl + HCl+ reaction

Cited by 9 publications

References 50 publications

Reaction mechanism conversion induced by the contest of nucleophile and leaving group

Reaction mechanism conversion induced by the contest of nucleophile and leaving group

Active Machine Learning for Chemical Dynamics Simulations. I. Estimating the Energy Gradient

Ab initio molecular dynamics benchmarking study of machine-learned potential energy surfaces for the HBr+ + HCl reaction

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