LIQUID METAL FREE ENERGIES FROM AB INITIO POTENTIAL SURFACES

2008 ◽  
Author(s):  
C. W. Greeff ◽  
R. Lizárraga ◽  
Mark Elert ◽  
Michael D. Furnish ◽  
Ricky Chau ◽  
...  
Keyword(s):  
Liquid Metal ◽  
Ab Initio ◽  
Free Energies ◽  
Potential Surfaces ◽  
Metal Free

scholarly journals Popular Integration Grids Can Result in Large Errors in DFT-Computed Free Energies

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Steven Wheeler
Keyword(s):  
Transition State ◽  
Molecular Orientation ◽  
Density Functional ◽  
Basis Sets ◽  
Free Energies ◽  
Functional Theory ◽  
Metal Free ◽  
Stereoselective Reactions ◽  
Functionalization Reaction ◽  
Transition State Structures

<div>Density functional theory (DFT) has emerged as a powerful tool for analyzing organic and organometallic systems and proved remarkably accurate in computing the small free energy differences that underpin many chemical phenomena (e.g. regio- and stereoselective reactions). We show that the lack of rotational invariance of popular DFT integration grids reveals large uncertainties in computed free energies for isomerizations, torsional barriers, and regio- and stereoselective reactions. The result is that predictions based on DFT-computed free energies for many systems can change qualitatively depending on molecular orientation. For example, for a metal-free propargylation of benzaldehyde, predicted enantioselectivities based on B97-D/def2-TZVP free energies using the popular (75,302) integration grid can vary from 62:38 to 99:1 by simply rotating the transition state structures. Relative free energies for the regiocontrolling transition state structures for an Ir-catalyzed C–H functionalization reaction computed using M06/6-31G(d,p)/LANL2DZ and the same grid can vary by more than 5 kcal mol–1, resulting in predicted regioselectivities that range anywhere from 14:86 to >99:1. Errors of these magnitudes occur for different functionals and basis sets, are widespread among modern applications of DFT, and can be reduced by using much denser integration grids than commonly employed.</div>

Summary, Conclusions, and Future Trends

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Electronic Structure ◽  
Complex Systems ◽  
Human Brain ◽  
Ab Initio ◽  
Large Scale ◽  
Dimensional Space ◽  
Electronic Structure Calculations ◽  
Potential Surfaces ◽  
The 1960S ◽  
Structure Calculations

Since the introduction of classical and semiclassical molecular dynamics (MD) methods in the 1960s and Gaussian procedures to conduct electronic structure calculations in the 1970s, a principal objective of theoretical chemistry has been to combine the two methods so that MD and quantum mechanical studies can be conducted on ab initio potential surfaces. Although numerous procedures have been attempted, the goal of first principles, ab initio dynamics calculations has proven to be elusive when the system contains five or more atoms moving in unrestricted three-dimensional space. For many years, the conventional wisdom has been that ab initio MD calculations for complex systems containing five or more atoms with several open reaction channels are presently beyond our computational capabilities. The rationale for this view are (a) the inherent difficulty of high level ab initio quantum calculations on complex systems that may take numerous, large-scale computations impossible, (b) the large dimensionality of the configuration space for such systems that makes it necessary to examine prohibitively large numbers of nuclear configurations, and (c) the extreme difficulty associated with obtaining sufficiently converged results to permit accurate interpolation of numerical data obtained from electronic structure calculations when the dimensionality of the system is nine or greater. Neural networks (NN) derive their name from the fact that their interlocking structure superficially resembles the neural network of a human brain and from the fact that NNs can sense the underlying correlations that exist in a database and properly map them in a manner analogous to the way a human brain can execute pattern recognition. Artificial neurons were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, an MIT logician. NNs have been employed by engineers for decades to assist in the solution of a multitude of problems. Nevertheless, the power of NNs to assist in the solution of numerous problems that occur in chemical reaction dynamics is just now being realized by the chemistry community.

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.

scholarly journals Global accurate diabatic potential surfaces for the reaction H + Li2

RSC Advances ◽  
2020 ◽  
Vol 10 (64) ◽  
pp. 39226-39240
Author(s):  
Ruilin Yin ◽  
Nan Gao ◽  
Jing Cao ◽  
Yanchun Li ◽  
Dequan Wang ◽  
...  
Keyword(s):  
Ab Initio ◽  
Adiabatic Potential ◽  
Basis Set ◽  
Potential Surfaces ◽  
High Level

The adiabatic potential energies for the lowest three states of a Li2H system are calculated with a high level ab initio method (MCSCF/MRCI) with a large basis set.

Design and Analysis of the Liquid Metal Free-Surface Divertor Cooling System

2019 ◽  
Vol 75 (8) ◽  
pp. 930-938 ◽  
Author(s):  
A. Khodak ◽  
C. Kessel ◽  
M. Tillack
Keyword(s):  
Free Surface ◽  
Liquid Metal ◽  
Cooling System ◽  
Metal Free
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