Skip to main content
SpringerLink
Log in

Comparison of algorithms for conical intersection optimisation using semiempirical methods

  • Regular Article
  • Published:
Theoretical Chemistry Accounts Aims and scope Submit manuscript

Abstract

We present a comparison of three previously published algorithms for optimising the minimum energy crossing point between two Born–Oppenheimer electronic states. The algorithms are implemented in a development version of the MNDO electronic structure package for use with semiempirical configuration interaction methods. The penalty function method requires only the energies and gradients of the states involved, whereas the gradient projection and Lagrange–Newton methods also require the calculation of non-adiabatic coupling terms. The performance of the algorithms is measured against a set of well-known small molecule conical intersections. The Lagrange–Newton method is found to be the most efficient, with the projected gradient method also competitive. The penalty function method can only be recommended for situations where non-adiabatic coupling terms cannot be calculated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bernardi F, Olivucci M and Robb MA (1996). Chem Soc Rev 25: 321–328

    Article  CAS  Google Scholar 

  2. Domcke W, Yarkony DR, Köppel H (eds) (2004) Conical intersections: electronic structure, dynamics and spectroscopy. Advanced series in physical chemistry, vol 15. World Scientific, Singapore

    Google Scholar 

  3. Schoenlein RW, Peteanu LA, Mathies RA and Shank CV (1991). Science 254: 412–415

    Article  CAS  Google Scholar 

  4. Koga N and Morokuma K (1985). Chem Phys Lett 119: 371–374

    Article  CAS  Google Scholar 

  5. Farazdel A and Dupuis M (1991). J Comput Chem 12: 276–282

    Article  CAS  Google Scholar 

  6. Ragazos IN, Robb MA, Bernardi F and Olivucci M (1992). Chem Phys Lett 197: 217–223

    Article  CAS  Google Scholar 

  7. Manaa MR and Yarkony DR (1993). J Chem Phys 99: 5251–5256

    Article  CAS  Google Scholar 

  8. Bearpark MJ, Robb MA and Schlegel HB (1994). Chem Phys Lett 223: 269–274

    Article  CAS  Google Scholar 

  9. Anglada JM and Bofill JM (1997). J Comput Chem 18: 992–1003

    Article  CAS  Google Scholar 

  10. Ciminelli C, Granucci G and Persico M (2004). Chem Eur J 10: 2327–2341

    Article  CAS  Google Scholar 

  11. Olivucci M, Lindh R and Vico L (2005). J Chem Theory Comput 1: 1029–1037

    Article  Google Scholar 

  12. Levine BG, Ko C, Quenneville J and Martínez TJ (2006). Mol Phys 104: 1039–1051

    Article  CAS  Google Scholar 

  13. Thiel W (2007) MNDO program, version 6.1, Mülheim

  14. Fletcher R (1987). Practical methods of optimization. Wiley, Chichester

    Google Scholar 

  15. Koslowski A, Beck ME and Thiel W (2003). J Comput Chem 24: 714–726

    Article  CAS  Google Scholar 

  16. Patchkovskii S, Koslowski A and Thiel W (2005). Theor Chem Acc 114: 84–89

    Article  CAS  Google Scholar 

  17. Yarkony DR and Lengsfield BH (1992). Adv Chem Phys 82: 1–71

    Article  CAS  Google Scholar 

  18. Izzo R and Klessinger M (2000). J Comput Chem 21: 52–62

    Article  CAS  Google Scholar 

  19. Toniolo A, Ben-Nun M and Martínez TJ (2002). J Phys Chem A 106: 4679–4689

    Article  CAS  Google Scholar 

  20. Yarkony DR (2004). J Phys Chem A 108: 3200–3205

    Article  CAS  Google Scholar 

  21. Murtagh BA and Sargent RWH (1970). Comput J 13: 185–194

    Article  Google Scholar 

  22. Barbatti M, Paier J and Lischka H (2004). J Chem Phys 121: 11614–11624

    Article  CAS  Google Scholar 

  23. Olivucci M, Ragazos IN, Bernardi F and Robb MA (1993). J Am Chem Soc 115: 3710–3721

    Article  CAS  Google Scholar 

  24. Cui Q and Morokuma K (1997). J Chem Phys 107: 4951–4959

    Article  CAS  Google Scholar 

  25. Yamamoto N, Bernardi F, Bottoni A, Olivucci M, Robb MA and Wilsey S (1994). J Am Chem Soc 116: 2064–2074

    Article  CAS  Google Scholar 

  26. Page CS and Olivucci M (2003). J Comput Chem 24: 298–309

    Article  CAS  Google Scholar 

  27. Palmer IJ, Ragazos IN, Bernardi F, Olivucci M and Robb MA (1993). J Am Chem Soc 115: 673–682

    Article  CAS  Google Scholar 

  28. Bearpark MJ, Bernardi F, Clifford S, Olivucci M, Robb MA, Smith BR and Vreven T (1996). J Am Chem Soc 118: 169–175

    Article  CAS  Google Scholar 

  29. Bearpark MJ, Celani P, Jolibois F, Olivucci M, Robb MA and Bernardi F (1999). Mol Phys 96: 645–652

    Article  CAS  Google Scholar 

  30. Barbatti M, Aquino AJA and Lischka H (2006). Mol Phys 104: 1053–1060

    Article  CAS  Google Scholar 

  31. Garavelli M, Celani P, Bernardi F, Robb MA and Olivucci M (1997). J Am Chem Soc 119: 6891–6901

    Article  CAS  Google Scholar 

  32. Garavelli M, Vreven T, Celani P, Bernardi F, Robb MA and Olivucci M (1998). J Am Chem Soc 120: 1285–1288

    Article  CAS  Google Scholar 

  33. Weber W (1996) Ph.D. Thesis, Universität Zürich

  34. Weber W and Thiel W (2000). Theor Chem Acc 103: 495–506

    CAS  Google Scholar 

  35. Kabsch W (1976). Acta Cryst A 32: 922–923

    Article  Google Scholar 

  36. Kabsch W (1978). Acta Cryst A 34: 827–828

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany

    Thomas W. Keal, Axel Koslowski & Walter Thiel

Authors
  1. Thomas W. Keal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Axel Koslowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Walter Thiel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Walter Thiel.

Electronic supplementary material

Below are the electronic supplementary materials.

ESM 1 (PDF 123 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Keal, T.W., Koslowski, A. & Thiel, W. Comparison of algorithms for conical intersection optimisation using semiempirical methods. Theor Chem Account 118, 837–844 (2007). https://doi.org/10.1007/s00214-007-0331-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00214-007-0331-5

Keywords

Search

Navigation