Skip to main content
Log in

Prediction of the lowest energy configuration for Lennard-Jones clusters

  • Articles
  • Published:
Science China Chemistry Aims and scope Submit manuscript

Abstract

Based on the work of previous researchers, a new unbiased optimization algorithm—the dynamic lattice searching method with two-phase local search and interior operation (DLS-TPIO) —is proposed in this paper. This algorithm is applied to the optimization of Lennard-Jones (LJ) clusters with N = 2–650, 660, and 665–680. For each case, the putative global minimum reported in the Cambridge Cluster Database (CCD) is successfully found. Furthermore, for LJ533 and LJ536, the potential energies obtained in this study are superior to the previous best results. In DLS-TPIO, a combination of the interior operation, two-phase local search method and dynamic lattice searching method is adopted. At the initial stage of the optimization, the interior operation reduces the energy of the cluster, and gradually makes the configuration ordered by moving some surface atoms with high potential energy to the interior of the cluster. Meanwhile, the two-phase local search method guides the search to the more promising region of the configuration space. In this way the success rate of the algorithm is significantly increased. At the final stage of the optimization, in order to decrease energy of the cluster further, the positions of surface atoms are further optimized by using the dynamic lattice searching method. In addition, a simple new method to identify the central atom of icosahedral configurations is also presented. DLS-TPIO has higher computing speed and success rates than some well-known unbiased optimization methods in the literature.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Doye JPK. Physical perspectives on the global optimization of atomic clusters. In: Pinter J D, eds. Global Optimization: Scientific and Engineering Case Studies. Berlin: Springer-Verlag, 2006. 103–139

    Google Scholar 

  2. Cheng LJ, Feng Y, Yang J, Yang JL. Funnel hopping: Searching the cluster potential energy surface over the funnels. J Chem Phys, 2009, 130: 214112

    Article  Google Scholar 

  3. Fa W, Luo CF, Dong JM. Bulk fragment and tubelike structures of AuN (N = 2–26). Phys Rev B, 2005, 72: 205428

    Article  Google Scholar 

  4. Wales DJ, Doye JPK. Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J Phys Chem A, 1997, 101: 5111–5116

    Article  CAS  Google Scholar 

  5. Wales DJ, Scheraga HA. Global optimization of clusters, crystals and biomolecules. Science, 1999, 285: 1368–1372

    Article  CAS  Google Scholar 

  6. Leary RH. Global optimization on funneling landscapes. J Global Optim, 2000, 18: 367–383

    Article  Google Scholar 

  7. Leary RH. Tetrahedral global minimum for the 98-atom Lennard-Jones cluster. Phys Rev E, 1999, 60: R6320–R6322

    Article  CAS  Google Scholar 

  8. Xue GL. Molecular conformation on the CM-5 by parallel two-level simulated annealing. J Global Optim, 1994, 4: 187–208

    Article  Google Scholar 

  9. Wille LT. Simulated annealing and the topology of the potential energy surface of Lennard-Jones clusters. Comput Mater Sci, 2000, 17: 551–554

    Article  CAS  Google Scholar 

  10. Krivov SV. Hierarchical global optimization of quasiseparable systems: Application to Lennard-Jones clusters. Phys Rev E, 2002, 66: 025701

    Article  Google Scholar 

  11. Deaven DM, Tit N, Morris JR. Structure optimization of Lennard-Jones clusters by a genetic algorithm. Chem Phys Lett, 1996, 256: 195–200

    Article  CAS  Google Scholar 

  12. Wolf MD, Landman U. Genetic algorithms for structural cluster optimization. J Phys Chem A, 1998, 102: 6129–6137

    Article  CAS  Google Scholar 

  13. Hartke B. Global cluster geometry optimization by a phenotype algorithm with niches: Location of elusive minima, and lower-order scaling with cluster size. J Comput Chem, 1999, 20: 1752–1759

    Article  CAS  Google Scholar 

  14. Romero D, Barron C, Gomez S. The optimal geometry of Lennard-Jones clusters: 148–309. Comput Phys Commun, 1999, 123: 87–96

    Article  CAS  Google Scholar 

  15. Xiang YH, Jiang HY, Cai WS, Shao XG. An efficient method based on lattice construction and the genetic algorithm for optimization of large Lennard-Jones clusters. J Phys Chem A, 2004, 108: 3586–3592

    Article  CAS  Google Scholar 

  16. Lee J, Lee I, Lee J. Unbiased global optimization of Lennard-Jones clusters for N ⩽ 201 using the conformational space annealing method. Phys Rev Lett, 2003, 91: 080201

    Article  Google Scholar 

  17. Cheng LJ, Cai WS, Shao XG. A connectivity table for cluster similarity checking in the evolutionary optimization method. Chem Phys Lett, 2004, 389: 309–314

    Article  CAS  Google Scholar 

  18. Jiang HY, Cai WS, Shao XG. A random tunneling algorithm for the structural optimization problem. Phys Chem Chem Phys, 2002, 4: 4782–4788

    Article  CAS  Google Scholar 

  19. Takeuchi H. Clever and efficient method for searching optimal geometries of Lennard-Jones clusters. J Chem Inf Model, 2006, 46: 2066–2070

    Article  CAS  Google Scholar 

  20. Shao XG, Cheng LJ, Cai WS. A dynamic lattice searching method for fast optimization of Lennard-Jones clusters. J Comput Chem, 2004, 25: 1693–1698

    Article  CAS  Google Scholar 

  21. Yang XL, Cai WS, Shao XG. A dynamic lattice searching method with constructed core for optimization of Lennard-Jones clusters. J Comput Chem, 2007, 28: 1427–2433

    Article  CAS  Google Scholar 

  22. Shao XG, Yang XL, Cai WS. A dynamic lattice searching method with interior operation for unbiased optimization of large Lennard-Jones clusters. J Comput Chem, 2008, 29: 1772–1779

    Article  CAS  Google Scholar 

  23. Wu X, Cai WS, Shao XG. A dynamic lattice searching method with rotation operation for optimization of large clusters. Chem Phys, 2009, 363: 72–77

    Article  CAS  Google Scholar 

  24. Cheng LJ, Cai WS, Shao XG. An energy-based perturbation and a taboo strategy for improving the searching ability of stochastic structural optimization methods. Chem Phys Lett, 2005, 404: 182–186

    Article  CAS  Google Scholar 

  25. Pullan W. An unbiased population-based search for the geometry optimization of Lennard-Jones Clusters: 2 ⩽ N ⩽ 372. J Comput Chem, 2005, 26: 899–906

    Article  CAS  Google Scholar 

  26. Cai WS, Shao XG. A fast annealing evolutionary algorithm for global optimization. J Comput Chem, 2002, 23: 427–435

    Article  CAS  Google Scholar 

  27. Northby JA. Structure and binding of Lennard-Jones clusters: 13 ⩽ N ⩽ 147. J Chem Phys, 1987, 87: 6166–6177

    Article  CAS  Google Scholar 

  28. Xiang YH, Cheng LJ, Cai WS, Shao XG. Structural distribution of Lennard-Jones clusters containing 562 to 1000 atoms. J Phys Chem A, 2004, 108: 9516–9520

    Article  CAS  Google Scholar 

  29. Shao XG, Xiang YH, Cai WS. Structural transition from icosahedra to decahedra of Lennard-Jones clusters. J Phys Chem A, 2005, 109: 5193–5197

    Article  CAS  Google Scholar 

  30. Locatelli M, Schoen F. Efficient algorithms for large scale global optimization: Lennard-Jones clusters. Comput Optim Appl, 2003, 26: 173–190

    Article  Google Scholar 

  31. Doye JPK, Leary RH, Locatelli M, Schoen F. The global optimization of Morse clusters by potential energy transformations. INFORMS J Comput, 2004, 16: 371–379

    Article  Google Scholar 

  32. Cassioli A, Locatelli M, Schoen F. Global optimization of binary Lennard-Jones clusters. Optim Method Softw, 2009, 24: 819–835

    Article  Google Scholar 

  33. Cheng LJ, Yang JL. Global minimum structures of Morse clusters as a function of range of the potential: 81 ⩽ N ⩽ 160. J Phys Chem A, 2007, 111: 5287–5293

    Article  CAS  Google Scholar 

  34. Cheng LJ, Cai WS, Shao XG. Geometry optimization and conformational analysis of (C60)N clusters by using a dynamic lattice searching method. ChemPhysChem, 2005, 6: 261–266

    Article  CAS  Google Scholar 

  35. Zhan H, Cheng LJ, Cai WS, Shao XG. Structural optimization of silver clusters from Ag61 to Ag120 by dynamic lattice searching method. Chem Phys Lett, 2006, 422: 358–362

    Article  CAS  Google Scholar 

  36. Shao XG, Xiang YH, Cai WS. Formation of central vacancy in icosahedral Lennard-Jones clusters. Chem Phys, 2004, 305: 69–75

    Article  CAS  Google Scholar 

  37. Liu HH, Jiang EY, Bai HL, Wu P, Li ZQ. Impact of atomic shells on the structure of clusters. Chem Phys Lett, 2005, 412: 195–199

    Article  CAS  Google Scholar 

  38. Liu DC, Nocedal J. On the limited memory BFGS method for large scale optimization. Math Program, 1989, 45: 503–528

    Article  Google Scholar 

  39. Doye JPK. The effect of compression on the global optimization of atomic clusters. Phys Rev E, 2000, 62: 8753–8761

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to XiangJing Lai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lai, X., Xu, R. & Huang, W. Prediction of the lowest energy configuration for Lennard-Jones clusters. Sci. China Chem. 54, 985–991 (2011). https://doi.org/10.1007/s11426-011-4280-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11426-011-4280-4

Keywords

Navigation