Semi-empirical quantum chemistry method

Introduction

Semi-empirical quantum chemistry methods are based on the Hartree–Fock formalism, but make many approximations and obtain some parameters from empirical data. They are very important in computational chemistry for treating large molecules where the full Hartree–Fock method without the approximations is too expensive. The use of empirical parameters appears to allow some inclusion of electron correlation effects into the methods.

Within the framework of Hartree–Fock calculations, some pieces of information (such as two-electron integrals) are sometimes approximated or completely omitted. In order to correct for this loss, semi-empirical methods are parametrized, that is their results are fitted by a set of parameters, normally in such a way as to produce results that best agree with experimental data, but sometimes to agree with ab initio results.

Type of simplifications used

Semi-empirical methods follow what are often called empirical methods where the two-electron part of the Hamiltonian is not explicitly included. For π-electron systems, this was the Hückel method proposed by Erich Hückel.^[1][2][3] For all valence electron systems, the extended Hückel method was proposed by Roald Hoffmann.^[4]

Semi-empirical calculations are much faster than their ab initio counterparts, mostly due to the use of the zero differential overlap approximation. Their results, however, can be very wrong if the molecule being computed is not similar enough to the molecules in the database used to parametrize the method.

Preferred application domains

Semi-empirical calculations have been most successful in the description of organic chemistry, where only a few elements are used extensively and molecules are of moderate size. However, semi-empirical methods were also applied to solids^[5] and nanostructures^[6] but with different parameterization.

Empirical research is a way of gaining knowledge by means of direct and indirect observation or experience. As with empirical methods, we can distinguish methods that are:

Methods restricted to π-electrons

These method exist for the calculation of electronically excited states of polyenes, both cyclic and linear. These methods, such as the Pariser–Parr–Pople method (PPP), can provide good estimates of the π-electronic excited states, when parameterized well.^[7][8] Indeed, for many years, the PPP method outperformed ab initio excited state calculations.

Methods restricted to all valence electrons.

These methods can be grouped into several groups:

• Methods such as CNDO/2, INDO and NDDO that were introduced by John Pople.^[9][10][11] The implementations aimed to fit, not experiment, but ab initio minimum basis set results. These methods are now rarely used but the methodology is often the basis of later methods.

• Methods that are in the MOPAC, AMPAC, and/or SPARTAN computer programs originally from the group of Michael Dewar.^[12] These are MINDO, MNDO,^[13] AM1,^[14] PM3,^[15] RM1[16] , PM6^[17] and SAM1. Here the objective is to use parameters to fit experimental heats of formation, dipole moments, ionization potentials, and geometries.

• Methods whose primary aim is to predict the geometries of coordination compounds, such as Sparkle/AM1, available for lanthanide complexes.

• Methods whose primary aim is to calculate excited states and hence predict electronic spectra. These include ZINDO and SINDO.^[18][19]

the latter being by far the largest group of methods.

See also

• List of quantum chemistry and solid-state physics software

References

1. E. Hückel, Zeitschrift für Physik, 70, 204, (1931); 72, 310, (1931); 76, 628 (1932); 83, 632, (1933)
2. Hückel Theory for Organic Chemists, C. A. Coulson, B. O'Leary and R. B. Mallion, Academic Press, 1978.
3. Andrew Streitwieser, Molecular Orbital Theory for Organic Chemists, Wiley, New York, (1961)
4. R. Hoffmann, Journal of Chemical Physics, 39, 1397, (1963)
5. Mudar A. Abdulsattar and Khalil H. Al-Bayati, ‘Corrections and parameterization of semiempirical large unit cell method for covalent semiconductors’, Phys. Rev. B 75, 245201 (2007).
6. Mudar A. Abdulsattar, ‘Size effects of semiempirical large unit cell method in comparison with nanoclusters properties of diamond-structured covalent semiconductors’, Physica E 41, 1679 (2009).
7. R. Pariser and R. Parr, Journal of Chemical Physics, 21, 466, 767, (1953)
8. J. A. Pople, Transactions of the Faraday Society, 49, 1375, (1953)
9. J. Pople and D. Beveridge, Approximate Molecular Orbital Theory, McGraw–Hill, 1970.
10. Ira Levine, Quantum Chemistry, Prentice Hall, 4th edition, (1991), pg 579–580
11. C. J. Cramer, Essentials of Computational Chemistry, Wiley, Chichester, (2002), pg 126–131
12. J. J. P. Stewart, Reviews in Computational Chemistry, Volume 1, Eds. K. B. Lipkowitz and D. B. Boyd, VCH, New York, 45, (1990)
13. Michael J. S. Dewar & Walter Thiel (1977). "Ground states of molecules. 38. The MNDO method. Approximations and parameters". Journal of the American Chemical Society. ACS Publications. 99 (15): 4899–4907. doi:10.1021/ja00457a004.
14. Michael J. S. Dewar; Eve G. Zoebisch; Eamonn F. Healy; James J. P. Stewart (1985). "Development and use of quantum molecular models. 75. Comparative tests of theoretical procedures for studying chemical reactions". Journal of the American Chemical Society. ACS Publications. 107 (13): 3902–3909. doi:10.1021/ja00299a024.
15. James J. P. Stewart (1989). "Optimization of parameters for semiempirical methods I. Method". The Journal of Computational Chemistry. Wiley InterScience. 10 (2): 209–220. doi:10.1002/jcc.540100208.
16. Gerd B. Rocha; Ricardo O. Freire; Alfredo M. Simas; James J. P. Stewart (2006). "RM1: A reparameterization of AM1 for H, C, N, O, P, S, F, Cl, Br, and I". The Journal of Computational Chemistry. Wiley InterScience. 27 (10): 1101–1111. doi:10.1002/jcc.20425. PMID 16691568.
17. James J. P. Stewart (2007). "Optimization of Parameters for Semiempirical Methods V: Modification of NDDO Approximations and Application to 70 Elements". The Journal of Molecular Modeling. Springer. 13 (12): 1173–1213. doi:10.1007/s00894-007-0233-4.
18. M. Zerner, Reviews in Computational Chemistry, Volume 2, Eds. K. B. Lipkowitz and D. B. Boyd, VCH, New York, 313, (1991)
19. Nanda, D. N. and Jug, K., Theoretica Chimica Acta, 57, 95, (1980)