Electronic energy partitioning in reactions occurring on more than one potential energy surface: Metastable Mg(3P) atoms with halogen molecules

1983 ◽  
Vol 78 (9) ◽  
pp. 5612-5620 ◽  
Author(s):  
Andrzej Kowalski ◽  
Michael Menzinger
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Electronic Energy ◽  
Energy Partitioning

Product Energy Distributions

1996 ◽  
Author(s):  
Tomas Baer ◽  
William L. Hase
Keyword(s):  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Vibrational Energy ◽  
Activation Barrier ◽  
Dissociation Rate ◽  
Energy Partitioning ◽  
The Other ◽  
Energy Distributions

The measurement of product translational and rotational energies, and in some cases vibrational energy, is often more readily accomplished than the measurement of the dissociation rate. As a result there exists a considerable body of experimental information about product energy distributions (FED) for many classes of reactions. The only simple model for treating these FED is the statistical one; however, there is a considerable diversity in its application. In the dissociation of large molecules at moderate to large excess energies, the translational, rotational, and vibrational energy distributions can be treated as continuous functions. On the other hand, in the dissociation of triatomic molecules, it is often possible to measure the quantized rotational energy distribution for specific vibrational energy levels of the diatomic product. Just as in the determination of the dissociation rates, product energy partitioning is highly sensitive to the potential energy surface. If there is no reverse activation barrier, the product energies are often distributed statistically. That is, the distributions depend only upon the product phase space and are independent of the detailed shape of the potential energy surface. On the other hand, for reactions with a “tight” transition state located at the top of a reverse activation barrier, statistical redistribution of the product energies is often not possible. After passing through the transition-state region, the products move down the repulsive wall and rapidly dissociate with little chance to exchange and equilibrate the available energy. Often, such products are ejected with considerable translational energy. This happens in large as well as small molecules or ions. The resulting product energy partitioning is then highly nonstatistical, even though the dissociation rate is perfectly predicted by RRKM theory. That is, the dissociation rate and product energy partitioning are separate and uncoupled events. The rate is governed early in the reaction history by the structure of the transition state, while product energy partitioning is determined late in the reaction and is governed by the shape of the potential energy surface at large internuclear distances. The most effective model for treating product energy distributions (PEDs) of reactions with no reverse activation barriers is the statistical theory.

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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