Empirical potential energy surfaces relating structure and activation energy. 2. Determination of transition-state structure for the spontaneous hydrolysis of axial tetrahydropyranyl acetals

1988 ◽  
Vol 110 (22) ◽  
pp. 7291-7299 ◽  
Author(s):  
Hans Beat. Buergi ◽  
Katharina C. Dubler-Steudle
Keyword(s):  
Activation Energy ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
State Structure ◽  
Transition State Structure ◽  
Empirical Potential ◽  
Spontaneous Hydrolysis ◽  
Energy Surfaces ◽  
Hydrolysis Of

scholarly journals DFT Study on Adsorption of Acetone and Toluene on Silicene

2020 ◽  
Vol 36 (1) ◽  
Author(s):  
Pham Trong Lam ◽  
Ta Thi Luong ◽  
Vo Van On ◽  
An Dinh Van
Keyword(s):  
Charge Transfer ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Adsorption Mechanism ◽  
Simulation Method ◽  
Dft Study ◽  
Gap Opening ◽  
Energy Surfaces ◽  
And Diffusion

In this work, we investigated the adsorption mechanism of acetone and toluene on the surface of silicene by the quantum simulation method. The images of the potential energy surfaces for different positions of the adsorbate on the silicene surface were explored by Computational DFT-based Nanoscope tool for determination of the most stable configurations and diffusion possibilities. The charge transfer in order of 0.2 – 0.3 electrons and the tunneling gap opening of 18 – 23 meV due to acetone and toluene, respectively, suggest that silicene is considerably sensitive with these VOCs and can be used as the material in the fabrication of reusable VOC sensors.

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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