Subject: ANS: AMSOL performance

From: <[email-masked]>
Date: Fri, 21 Oct 1994 19:27:52 -0500 (CDT)

Steve,
> 
> I am running Amsol 4.5 on an Indigo2 (150Mhz), trying to do solvation 
> energies of compounds with ca. 15 heavy atoms.  These systems optimize for 
> the gas phase in about 2 cpu minutes using the keyword "AM1".  For the 
> solvation, I am using "AM1 SM2" and 4 cpu _hours_ later, things are still 
> chugging along.  I have no experience with the solvation models, and would 
> like know what order of magnitude of cpu times I should be expecting.  Also
> , are there keyword combinations which will reduce this (I haven't tinkered 
> with e.g. PULAY, since the gas phase calculation went so quickly)?
> 
   I feel I can answer this question fairly authoritatively . . . 

   The SMx solvation models as presently designed do not allow for analytical
first derivatives. We are working on this, but for the moment the only way to
do an optimization is by brute force finite differences. (Actually, even the
gas-phase optimizations are not truly analytical, but they get away with a
trick at the NDDO level that fails to be useful for solvation).

   While this might sound depressing, there is a flip side to the coin. 
As a rule, reoptimization in solution adds less than 5% to the total
solvation free energy relative to that calculated for the gas-phase geometry
(the only exception is for very polarizable systems, where small geometrical
changes can allow significant distortion of the electronic structure). Many
reports in the literature have appeared to this effect by other authors using
our models, and we have made these observations too (I'll send you refs if
you want, but don't want to waste bandwidth on the net). Since that 5% is
typically smaller than the intrinsic error in the model, it is negligible.
The frozen geometry calculations only take a few cpu seconds for a system of
the size you mentioned, so it is trivial to do the 1SCF calculations, and
then decide if you want to invest more time in the full optimization.

   I add as a final caveat that we are always skeptical about semiempirical
geometries after having found time and again some fairly dramatic error
in structure (especially intramolecular hydrogen bonds, five-membered rings,
amides, and transition states that can exhibit biradicaloid character). Our
preferred approach these days is to optimize at a more trustworthy level
(depending on your system, that may be anything from a force-field to
converged QM calculations) and then add solvation from an SMx calculation at
that frozen geometry.

   In closing, there are electrostatics-only continuum models that permit
analytical energy derivatives (e.g., the rather crude Onsager model available
in G92 and more refined versions that I believe are slated for release
sometime relatively soon, also Tomasi's PCM approach or Rivail's multipole
expansion in idealized cavities, coded into GAMESS-UK and the former perhaps
in MONSTERGAUSS). If you are dealing with molecules that do not have
stationary gas-phase structures (e.g., an amino acid zwitterion) then you
might consider such an approach. The virtue of the SMx models, however,
is that they have been developed to simultaneously account for 
non-electrostatic solvation effects, and thus predict solvation free 
energies that may be compared directly to experiment (Version 4.5 contains 
only water models, soon-to-be-released version 5.0 also contains 
hydrocarbon models, other solvents are in various stages of current 
development).

Best regards,

Chris

P.S. You'll find the above summarized into one sentence in section 8 of the
users' manual (which illustrates a nameless universal truth . . . )
-- 

Christopher J. Cramer
University of Minnesota
Department of Chemistry
207 Pleasant St. SE
Minneapolis, MN 55455-0431
(612) 624-0859
[email-masked]