Hostname: page-component-848d4c4894-xm8r8 Total loading time: 0 Render date: 2024-06-26T10:54:43.406Z Has data issue: false hasContentIssue false

Ab Initio Calculations on Porphyrins in the Condensed Phase

Published online by Cambridge University Press:  10 February 2011

P.N. Day
Affiliation:
Air Force Research Laboratory, Materials Directorate, Wright-Patterson AFB, OH 45433, daypaul@biotech.ml.wpafb.af.mil, wangz@biotech.ml.wpafb.af.mil, pachterr@ml.wpafb.af.mil
Z. Wang
Affiliation:
Air Force Research Laboratory, Materials Directorate, Wright-Patterson AFB, OH 45433, daypaul@biotech.ml.wpafb.af.mil, wangz@biotech.ml.wpafb.af.mil, pachterr@ml.wpafb.af.mil
R. Pachter
Affiliation:
Air Force Research Laboratory, Materials Directorate, Wright-Patterson AFB, OH 45433, daypaul@biotech.ml.wpafb.af.mil, wangz@biotech.ml.wpafb.af.mil, pachterr@ml.wpafb.af.mil
Get access

Abstract

Porphyrins are a promising class of materials for optical limiting applications, and in the condensed phase solvent effects have been shown to be significant. We report results with a method designed to simulate the effects of discrete solvent molecules, namely the effective fragment potential (EFP) approach which has been implemented for use in ab initio calculations. Further, a simulated annealing (SA) method has been implemented with the EFP solvation model in an attempt to solve the problem of multiple minima in clusters of molecules. The results with this method indicate some success on models of aqueous formamide and aqueous glutamic acid. Ab initio calculations can now be carried out on porphyrins, and the solvation methods are being updated for their use on these systems.

Type
Research Article
Copyright
Copyright © Materials Research Society 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Kirkwood, J. G., J. Chem. Phys. 2, 351 (1934); L. Onsager,. J. Am. Chem. Soc. 58, 1486 (1936); O. Tapia, O. Goscinski, Mol. Phys. 29, 1653 (1975); M. M. Karelson, A. R. Katritzky, M. C. Zerner, Int. J. Quantum Chem. Symp. 20, 521 (1986); K. V. Mikkelsen, H. Aagren, H. J. A. Jensen, T. Helgaker, J. Chem. Phys. 89, 3086 1988); M. W. Wong, M. J. Frisch, K. B. Wiberg, J. Am. Chem. Soc. 113, 4776 (1991); M. Szafran, M. Karelson, A. R. Katritzky, J. Koput, M. C. Zerner, J. Comput. Chem. 14, 371 (1993); M. Karelson, T. Tamm, M. C. Zerner, J. Phys. Chem. 97, 11901 (1993).Google Scholar
2. Jensen, J. H., Day, P. N., Gordon, M. S., Basch, H., ; Cohen, D., Garmer, D. R., Kraus, M., Stevens, W. J., Modeling the Hydrogen Bond, edited by D. A. Smith, ACS Symp. Series 569, 1994, p139; P. N. Day, J. H. Jensen, M. S. Gordon, S. P. Webb, W. J. Stevens, M. Krauss, D. Garmer, H. Basch, and D. Cohen; J. Chem. Phys. 105, 1968 (1996).Google Scholar
3. Schmidt, M. W., Baldridge, K. K., Boatz, J. A., Elbert, S. T., Gordon, M. S., Jensen, J. H., Koseki, S., Matsunaga, N., Nguyen, K. A., Su, S., Windus, T. L., Dupuis, M., and Montgomery, J. A., J. Comput. Chem. 14, 1347 (1993).Google Scholar
4. Chen, W., Gordon, M. S., J. Chem. Phys. 105, 11081 (1996).Google Scholar
5. Day, P. N., Pachter, R., J. Chem. Phys. 107, 2990 (1997).Google Scholar
6.. Jensen, J. H. and Gordon, M. S., Mol. Phys. 5, 1313 (1996).Google Scholar
7. Kirkpatrick, S., Gelatt, C. D. Jr., and Vecchi, M. P., Science 220, 671 (1983).Google Scholar
8. Goldberg, D., Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, MA, 1989.Google Scholar
9. Day, P. N., Wang, Z., and Pachter, R. in Materials for Optical Limiting II, edited by Hood, P., Pachter, R., Lewis, K., Perry, J.W., Hagan, D., Sutherland, R. (Mater. Res. Soc. Proc. 479, Pittsburgh, PA 1997), p. 307312.Google Scholar
10. Parks, G. T., Nucl. Technol. 89, 233 (1990).Google Scholar
11 Su, W., private correspondence.Google Scholar