Molecular dynamics simulations and hyperspherical mode analysis of NO in Kr crystals with the use of ab initio potential energy surfaces for the Kr-NO complex

2008 ◽  
Vol 108 (10) ◽  
pp. 1821-1830 ◽  
Author(s):  
Juan Carlos Castro Palacio ◽  
Jesús Rubayo-Soneira ◽  
Andrea Lombardi ◽  
Vincenzo Aquilanti
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Potential Energy Surfaces ◽  
Mode Analysis ◽  
Dynamics Simulations ◽  
Energy Surfaces

Reduced-dimensional surface hopping with offline–online computations

2021 ◽  
Author(s):  
Zachary Morrow ◽  
Hyuk-Yong Kwon ◽  
Carl Tim Kelley ◽  
Elena Jakubikova
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Molecular Dynamics Simulations ◽  
Potential Energy Surfaces ◽  
Energy Levels ◽  
Surface Hopping ◽  
Dynamics Simulations ◽  
Energy Surfaces ◽  
Nuclear Geometry ◽  
Dimensional Surface

Molecular dynamics simulations often classically evolve the nuclear geometry on adiabatic potential energy surfaces (PESs), punctuated by random hops between energy levels in regions of strong coupling, in an algorithm...

Path-integral molecular dynamics simulations of glycine·(H2O)n (n=1–7) clusters on semi-empirical PM6 potential energy surfaces

2009 ◽  
Vol 365 (1-2) ◽  
pp. 60-68 ◽  
Author(s):  
Takehiro Yoshikawa ◽  
Haruki Motegi ◽  
Akira Kakizaki ◽  
Toshiyuki Takayanagi ◽  
Motoyuki Shiga ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Molecular Dynamics Simulations ◽  
Path Integral ◽  
Potential Energy Surfaces ◽  
Dynamics Simulations ◽  
Energy Surfaces ◽  
Semi Empirical

Trajectory surface hopping molecular dynamics simulations for retinal protonated Schiff-base photoisomerization

2021 ◽  
Author(s):  
Yuxiu Liu ◽  
Chaoyuan Zhu
Keyword(s):  
Molecular Dynamics ◽  
Schiff Base ◽  
Quantum Yield ◽  
Potential Energy ◽  
Molecular Dynamics Simulations ◽  
Potential Energy Surfaces ◽  
Conical Intersection ◽  
Surface Hopping ◽  
Dynamics Simulations ◽  
Energy Surfaces

A global-switching trajectory surface hopping method on TDDFT potential energy surfaces has been used to simulate complex conical intersection networks and to predict photoproduct quantum yield distributions for a real RPSB system.

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>

Empirical Potential-Energy Surfaces Fitting Using Feed forward Neural Networks

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Force Fields ◽  
Pair Potential ◽  
Potential Surfaces ◽  
Empirical Potential ◽  
Functional Forms ◽  
Dynamics Simulations ◽  
Energy Surfaces

When the system of interest becomes too complex to permit the use of ab initio methods to obtain the system potential-energy surfaces (PES), empirical potential surfaces are frequently employed to represent the force fields present in the system under investigation. In most cases, the functional forms present in these potentials are selected on the basis of chemical and physical intuitions. The parameters of the surface are frequently adjusted to fit a very small set of experimental data that comprise bond energies, equilibrium bond distances and angles, fundamental vibrational frequencies, and perhaps measured barrier heights to reactions of interest. Such potentials generally yield only qualitative or semiquantitative descriptions of the system dynamics. Several research groups have significantly improved the accuracy of the values of the experimental properties computed using empirical potential surfaces by fitting the chosen functional form for the potential to the force fields obtained from trajectories using ab initio Car-Parrinello molecular dynamics simulations. The fitting to the force fields is usually done using a least-squares fitting approach. This method has been employed by Izvekov et al. to obtain effective non-polarizable three-site force fields for liquid water. Carré et al. have employed such a procedure to obtain a new pair potential for silica. In their investigation, the vector of potential parameters was fitted using an iterative Levenberg-Marquardt algorithm. Tangney and Scandolo have also developed an interatomic force field for liquid SiO2 in which the parameters were fitted to the forces, stresses, and energies obtained from ab initio calculations. Ercolessi and Adams have used a quasi-Newtonian procedure to fit an empirical potential for aluminum to data obtained from first-principals computations. Empirical potentials can be improved by making the parameters parameterized functions of the coordinates defining the instantaneous positions of the atoms of the system. This approach has been successfully employed by numerous investigators The difficulty with this procedure is that the number of parameters that must be adjusted increases rapidly. Appropriate fitting of these parameters requires a much more extensive database. Finally, the actual fitting process can often be tedious, difficult, and time-consuming.

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