Theoretical Investigations of Intramolecular Energy Transfer Rates and Pathways for Vinyl Bromide on an ab Initio Potential-Energy Surface

2001 ◽  
Vol 105 (11) ◽  
pp. 2147-2155 ◽  
Author(s):  
Asif Rahaman ◽  
Lionel M. Raff
Keyword(s):  
Energy Transfer ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Intramolecular Energy Transfer ◽  
Transfer Rates ◽  
Vinyl Bromide ◽  
Theoretical Investigations

Interaction forces and energy transfer dynamics of LiH (1gE+) and helium atoms. I. The ab initio evaluation of the lowest potential energy surface

1997 ◽  
Vol 215 (2) ◽  
pp. 227-238 ◽  
Author(s):  
F.A. Gianturco ◽  
Sanjay Kumar ◽  
Shashi K. Pathak ◽  
M. Raimondi ◽  
M. Sironi ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Interaction Forces ◽  
Helium Atoms

An accurate, global, ab initio potential energy surface for the H + 3 molecule

2000 ◽  
Vol 98 (5) ◽  
pp. 261-273 ◽  
Author(s):  
Oleg L. Polyansky, Rita Prosmiti, Wim Kl
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
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>

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