When refining transition states, and TS is not wanted, the McIver-Komornicki
gradient norm minimization [9,10]
routines, POWSQ and
SEARCH, can be used by including SIGMA. These are
very rapid routines, but do not work
for all species. If the gradient norm is low, i.e., less than about 5
units, then SIGMA will probably work; in most cases where TS
does not work, NLLSQ is
recommended. SIGMA first calculates a Hessian matrix, a
slow step, then works out the direction of fastest descent, and searches
along that direction until the gradient norm is minimized. The Hessian
is then partially updated in light of the new gradients, and a fresh
search direction found. Clearly, if the Hessian changes markedly as a
result of the line-search, the update done will be inaccurate, and the
new search direction will be faulty.
SIGMA should be avoided if at all possible when non-variationally
optimized calculations are being done.
If the Hessian is suspected to be corrupt within SIGMA, it will be
automatically recalculated. This frequently speeds up the rate at which
the transition state is located. If you do not want the Hessian to be
reinitialized--it is costly in CPU time--specify LET on the keyword
line.