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Part 1<br />

Improving Self-consistent Field Convergence<br />

⊥ ⊥<br />

Inserting Eq. (1.77) in the last term of Eq. (1.76) and neglecting the term Tr ∆ G ( ∆ ) , the<br />

augmented Roothaan-Hall energy model can be written as<br />

( ) ( ) ( )<br />

ARH ( ) RH ( ) ( ) RH<br />

<br />

E D = E D + EHF D0 − E ( D0 ) + Tr 2∆−∆ G ∆ , (1.80)<br />

where G ( ∆ ) is evaluated as a linear combination of previous Fock matrices<br />

n<br />

<br />

( ) ∑ωi ( i ) ∑ωi ( i )<br />

G ∆ = G D = ( F D − h ). (1.81)<br />

i= 1 i=<br />

1<br />

The energy model E ARH has no intrinsic restrictions with respect to how different the densities<br />

spanning the subspace are allowed to be, and this is one of the benefits compared to the TRSCF<br />

scheme. For the TRDSM energy model, the purification implicit in the DSM energy makes no sense<br />

if the densities are too different, in particular if they have different electron configurations. In ARH,<br />

configuration shifts can be handled without problems, and whereas old, obsolete densities pollute<br />

the DSM energy model, they simply disappear from the ARH energy model, since their weights ω i<br />

diminish.<br />

We expect a faster convergence rate for ARH compared to TRSCF, mainly because the RH and<br />

DSM steps are merged to an energy model with correct gradient (not just in the subspace) and an<br />

approximate Hessian, which is improved in each iteration using the information from the previous<br />

density and Fock matrices.<br />

1.4.3.2 The Augmented RH Optimization<br />

The density for which the ARH energy model should be optimized can be expanded in the antisymmetric<br />

matrix X<br />

n<br />

D ( X () () () ()<br />

) = exp 1<br />

( − XS ) D i 0 exp ( SX ) = D i ⎡ i 0<br />

+<br />

0 , ⎤ + ⎡⎡ i<br />

2 0<br />

, ⎤ , ⎤<br />

⎣ D X ⎦ ⎣ ⎦<br />

+<br />

⎣<br />

D X X ⎦<br />

, (1.82)<br />

() i<br />

S S S<br />

where D<br />

0<br />

is the reference density from which the step X is taken. Optimizing the ARH energy is<br />

thus a nonlinear problem and an iterative scheme should be applied.<br />

A Newton-Raphson (NR) optimization of the ARH energy is therefore carried out, and the steps are<br />

ARH<br />

found minimizing a second order approximation of the ARH energy E<br />

(2)<br />

by the preconditioned<br />

conjugate gradient (PCG) method. The second order approximation of the ARH energy, where the<br />

constant terms are excluded, can be written as<br />

34

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