28.02.2013 Views

Handbook of Solvents - George Wypych - ChemTech - Ventech!

Handbook of Solvents - George Wypych - ChemTech - Ventech!

Handbook of Solvents - George Wypych - ChemTech - Ventech!

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

8.7 Theoretical and computing modeling 471<br />

A point on the BO potential energy surface (PES) is then given by the minimum with respect<br />

to the ψ i <strong>of</strong> the energy functional:<br />

*<br />

[ { ψi}{ , I}{ , αv} ] = ∑∫ ψi() ⎢−<br />

∇ ⎥ ψi() r + U[ ρ(){ r , RI}{<br />

, αv}<br />

]<br />

3<br />

2<br />

V R d r r<br />

i<br />

Ω<br />

⎡<br />

⎣<br />

2<br />

h<br />

2m<br />

⎤<br />

⎦<br />

[8.91]<br />

where {R I} are the nuclear coordinates, {α v} are all the possible external constraints imposed<br />

on the system (e.g., the volume Ω). The functional U contains the inter-nuclear Coulomb<br />

repulsion and the effective electronic potential energy, including external nuclear,<br />

Hartree, and exchange and correlation contributions.<br />

In the conventional approach, the minimization <strong>of</strong> the energy functional (eq. [8.91])<br />

with respect to the orbitals ψ i subject to the orthonormalization constraint leads to a set <strong>of</strong><br />

self-consistent equations (the Kohn-Sham equations), i.e.:<br />

⎧ 2<br />

h<br />

⎫<br />

2 ∂U<br />

⎨−<br />

∇ + ⎬ ψi() r = εiψ i()<br />

r<br />

[8.92]<br />

⎩<br />

2m<br />

∂ρ()<br />

r<br />

⎭<br />

whose solution involves repeated matrix diagonalizations (and rapidly growing computational<br />

effort as the size <strong>of</strong> the system increases).<br />

It is possible to use an alternative approach, regarding the minimization <strong>of</strong> the functional<br />

as an optimization problem, which can be solved by means <strong>of</strong> the simulated annealing<br />

procedure. 71 A simulated annealing technique based on MD can be efficiently applied to<br />

minimize the KS functional: the resulting technique, called “dynamical simulated annealing”<br />

allows the study <strong>of</strong> finite temperature properties.<br />

In the “dynamical simulated annealing,” the {R i}, {α v} and {ψ i} parameters can be<br />

considered as dependent on time; then the Lagrangian is introduced, i.e.:<br />

with:<br />

L = ∑ ∫d<br />

r + ∑ M R + ∑ −V<br />

R<br />

1 3 2 1 1 2<br />

μ ψ� �<br />

μ α� ψ , , α<br />

2<br />

2 2<br />

∫<br />

Ω<br />

i<br />

Ω<br />

( ) ( )<br />

3 *<br />

d rψ rt , ψ rt , = δ<br />

i j ij<br />

[ { }{ }{ } ]<br />

i I I v v i I v<br />

I<br />

v<br />

[8.93]<br />

[8.94]<br />

In eq. [8.94] the dot indicates time derivative, MI are the nuclear masses andμ are arbitrary<br />

parameters having the dimension <strong>of</strong> mass.<br />

Using eq. [8.94] it is possible to generate a dynamics for {Ri}, {αv} and {ψi} through<br />

the following equations:<br />

MR<br />

��<br />

I I =−∇ R V<br />

[8.95]<br />

I<br />

∂<br />

μ α��<br />

V<br />

v v =−<br />

∂αv<br />

⎛ ⎞<br />

⎜<br />

⎟<br />

⎝ ⎠<br />

[8.96]

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!