scholarly journals Neural network potential energy surface for the low temperature ring polymer molecular dynamics of the H2CO + OH reaction

2021 ◽  
Vol 154 (9) ◽  
pp. 094305
Author(s):  
Pablo del Mazo-Sevillano ◽  
Alfredo Aguado ◽  
Octavio Roncero
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Low Temperature ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Ring Polymer ◽  
Ring Polymer Molecular Dynamics

Dynamics and kinetics of the Si(1D) + H2/D2 reactions on a new global ab initio potential energy surface

2021 ◽  
Vol 23 (10) ◽  
pp. 6141-6153
Author(s):  
Jianwei Cao ◽  
Yanan Wu ◽  
Haitao Ma ◽  
Zhitao Shen ◽  
Wensheng Bian
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Quantum Dynamics ◽  
Energy Surface ◽  
Molecular Dynamics Calculations ◽  
Ring Polymer ◽  
Ring Polymer Molecular Dynamics ◽  
Kinetics Of

Quantum dynamics and ring polymer molecular dynamics calculations reveal interesting dynamical and kinetic behaviors of an endothermic complex-forming reaction.

An accurate potential energy surface and ring polymer molecular dynamics study of the Cl + CH4 → HCl + CH3 reaction

2020 ◽  
Vol 22 (1) ◽  
pp. 344-353 ◽  
Author(s):  
Yang Liu ◽  
Jun Li
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Entrance Channel ◽  
Rate Coefficients ◽  
Spin Orbit ◽  
Ring Polymer ◽  
Ring Polymer Molecular Dynamics

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.

Kinetic study of the OH + HO2 → H2O + O2 reaction using ring polymer molecular dynamics and quantum dynamics

2020 ◽  
Vol 22 (41) ◽  
pp. 23657-23664
Author(s):  
Yang Liu ◽  
Hongwei Song ◽  
Jun Li
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Kinetic Study ◽  
Quantum Dynamics ◽  
Energy Surface ◽  
Title Reaction ◽  
Ring Polymer ◽  
Ring Polymer Molecular Dynamics ◽  
Kinetics Of

The kinetics of the title reaction is studied by running the ring polymer molecular dynamics and quantum dynamics on an accurate potential energy surface.

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

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