Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feedforward neural networks. II. Numerical application of the method

2007 ◽  
Vol 127 (13) ◽  
pp. 134105 ◽  
Author(s):  
M. Malshe ◽  
L. M. Raff ◽  
M. G. Rockley ◽  
M. Hagan ◽  
Paras M. Agrawal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Feedforward Neural Networks ◽  
Vibrationally Excited ◽  
Numerical Application ◽  
Vinyl Bromide ◽  
Dissociation Dynamics

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

2009 ◽  
Vol 130 (13) ◽  
pp. 134101 ◽  
Author(s):  
A. Pukrittayakamee ◽  
M. Malshe ◽  
M. Hagan ◽  
L. M. Raff ◽  
R. Narulkar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Force Fields ◽  
Feedforward Neural Networks ◽  
Simultaneous Fitting

A neural network potential energy surface for the F + H2O ↔ HF + OH reaction and quantum dynamics study of isotopic effect

2021 ◽  
Author(s):  
Xiaoren Zhang ◽  
Jun Chen ◽  
Xin Xu ◽  
Shu Liu ◽  
Dong H. Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Quantum Dynamics ◽  
Energy Surface ◽  
Isotopic Effect ◽  
The Neural Networks

A global potential energy surface for the F + H2O ↔ HF + OH reaction has been constructed using the neural networks method based on ~24,000 ab initio energies calculated...

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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