Genetic algorithm optimization of atomic clusters

The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.

[1]  Yong L. Xiao,et al.  Genetic algorithm: a new approach to the prediction of the structure of molecular clusters , 1993 .

[2]  Ho,et al.  Molecular geometry optimization with a genetic algorithm. , 1995, Physical review letters.

[3]  J. Northby.  Structure and binding of Lennard‐Jones clusters: 13≤N≤147 , 1987 .

[4]  N. J. A. Sloane,et al.  Minimal-energy clusters of hard spheres , 1995, Discret. Comput. Geom..

[5]  L. Wille,et al.  Computational complexity of the ground-state determination of atomic clusters , 1985 .

[6]  D. Tománek,et al.  Growth regimes of carbon clusters. , 1991, Physical review letters.

[7]  R. Smalley,et al.  FT‐ICR probes of silicon cluster chemistry: The special behavior of Si+39 , 1987 .

[8]  Andreoni,et al.  Structure of nanoscale silicon clusters. , 1994, Physical review letters.

[9]  Che Ting Chan,et al.  A transferable tight-binding potential for carbon , 1992 .

[10]  K. Ho,et al.  Structural optimization of Lennard-Jones clusters by a genetic algorithm , 1996 .

[11]  Ernö Pretsch,et al.  Application of genetic algorithms in molecular modeling , 1994, J. Comput. Chem..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  J. Doye,et al.  The effect of the range of the potential on the structures of clusters , 1995 .

[14]  M. V. Ramakrishna,et al.  Combined tight‐binding and density functional molecular dynamics investigation of Si12 cluster structure , 1996 .

[15]  J. Grossman,et al.  Structure and stability of molecular carbon: Importance of electron correlation. , 1995, Physical review letters.

[16]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[17]  Richard S. Judson,et al.  Analysis of the genetic algorithm method of molecular conformation determination , 1993, J. Comput. Chem..

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  L. T. Wille,et al.  Searching potential energy surfaces by simulated annealing , 1986, Nature.

[20]  Michael F. Herman.  The role of correlated many chain motions in linear chain polymer melts , 1995 .

[21]  E. Kaxiras.  Structural model for a covalently bonded Si45 cluster , 1989 .

[22]  Donald M. Cox,et al.  Production and characterization of supersonic carbon cluster beams , 1984 .

[23]  Milani,et al.  Simulated annealing of carbon clusters. , 1990, Physical review. B, Condensed matter.

[24]  D. M. Deaven,et al.  Genetic-algorithm energy minimization for point charges on a sphere. , 1996, Physical review. B, Condensed matter.

[25]  Bowei Zhang,et al.  Structures of large fullerenes: C60 to C94 , 1992 .

[26]  Williams,et al.  Method of constrained global optimization. , 1994, Physical review letters.

[27]  Erber,et al.  Comment on "Method of constrained global optimization" , 1995, Physical review letters.