Reviews in Computational Chemistry Volume 18
Reviews in Computational Chemistry Volume 18
Reviews in Computational Chemistry Volume 18
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Electronegativity Equalization Models 113<br />
Although a valence-type force field of the type illustrated by Eq. [1] is<br />
most suitable for model<strong>in</strong>g molecular systems, the electronegativity equalization<br />
approach to treat<strong>in</strong>g polarization can be coupled equally well to other<br />
types of potentials. Streitz and M<strong>in</strong>tmire 127 used an EE-based model <strong>in</strong> conjunction<br />
with an embedded atom method (EAM) potential to treat polarization<br />
effects <strong>in</strong> bulk metals and oxides. The result<strong>in</strong>g ES þ EAM model has<br />
been parameterized for alum<strong>in</strong>um and titanium oxides, and has been used<br />
to study both charge-transfer effects and reactivity at <strong>in</strong>terfaces. 127,128,160,161<br />
In most electronegativity equalization models, if the energy is quadratic<br />
<strong>in</strong> the charges (as <strong>in</strong> Eq. [36]), the m<strong>in</strong>imization condition (Eq. [41]) leads to a<br />
coupled set of l<strong>in</strong>ear equations for the charges. As with the polarizable po<strong>in</strong>t<br />
dipole and shell models, solv<strong>in</strong>g for the charges can be done by matrix <strong>in</strong>version,<br />
iteration, or extended Lagrangian methods.<br />
As with other polarizable models, the matrix methods tend to be avoided<br />
by most researchers because of their computational expense. And when they<br />
are used, the matrix <strong>in</strong>version is typically not performed at every step. 160,162<br />
Some EE applications have relied on iterative methods to determ<strong>in</strong>e the<br />
charges. 53,127 For very large-scale systems, multilevel methods are available.<br />
161,163 As with the dipole polarizable models, the proper treatment of<br />
long-range electrostatic <strong>in</strong>teractions is especially important for fluctuat<strong>in</strong>g<br />
charge models. 164 Monte Carlo methods have also been developed for use<br />
with fluctuat<strong>in</strong>g charge models. 162,165 Despite this variety of available techniques,<br />
the most common approach is to use a matrix <strong>in</strong>version or iterative<br />
method only to obta<strong>in</strong> the <strong>in</strong>itial energy-m<strong>in</strong>imiz<strong>in</strong>g charge distribution; an<br />
extended Lagrangian method is then used to propagate the charges dynamically<br />
<strong>in</strong> order to take advantage of its computational efficiency.<br />
In the extended Lagrangian method, as applied to a fluctuat<strong>in</strong>g charge<br />
system, 126 the charges are given a fictitious mass, Mq, and evolved <strong>in</strong> time<br />
accord<strong>in</strong>g to Newton’s equation of motion, analogous to Eq. [23],<br />
Mq qi ¼ @U<br />
where la is the average of the negative of the electronegativity of the molecule<br />
a conta<strong>in</strong><strong>in</strong>g atom i,<br />
la ¼<br />
1<br />
Na<br />
@qi<br />
la<br />
½49Š<br />
X<br />
wi wa ½50Š<br />
i 2 a<br />
Here, Na is the number of atoms <strong>in</strong> molecule a. Comb<strong>in</strong><strong>in</strong>g Eq. [49], [50], and<br />
[42], we have<br />
Mq qi ¼ w a w i ½51Š