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Xavier Andrade edited geometry optimization.tex
over 9 years ago
Commit id: ecbf7346675bbbd03a9c47dfb01ae3241c7b733b
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\dot{\vec{\mathrm{v}}}{(t)} = \dfrac{\vec{F}{(t)}}{m} - \dfrac{\alpha}{\Delta t}|\vec{\mathrm{v}}(t)|\left[\hat{\mathrm{v}}(t)-\hat{F}(t)\right]\ ,
\end{equation}
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where the second term is an introduced acceleration in a direction``steeper'' than the usual direction of motion. Obviously, if $\alpha = 0$ then $\vec{\mathrm{V}}(t) = \vec{v}(t)$, meaning the velocity modification vanish, and the acceleration
$\dot{\vec{v}}{(t)} $\dot{\vec{\mathrm{v}}}{(t)} = \vec{F}{(t)}/m$, as usual.
We illustrate the dynamic of the algorithm with a simple case: the geometry optimization of a methane molecule. The input geometry consist of one carbon atom at the center of a tetrahedral structure, and four hydrogen atoms at the vertices, where the initial C-H distance is 1.2~\AA. In Fig.~\ref{fig:go_fire} we plot the energy
difference $\Delta E_{\text{tot}}$ respect to the equilibrium conformation, the maximum component of the force acting on the ions $F_{\text{max}}$, and the C-H bond length of the molecule being optimized. On the first iterations the geometry approaches to the equilibrium position, but moves away on the 3th, this means a change on the direction of the gradient, therefore there is no movement on the 4th iteration, the adaptive parameters are reseted, resuming the sliding on the 5th iteration.