Global optimization

The current status of global optimization in computational molecular science is characterized in this article by focusing on one particular area, evolutionary algorithms applied to cluster structure optimization. Other algorithms and application areas are also mentioned briefly, indicating a widespread use of global optimization techniques in every conceivable branch of computational molecular science. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 879–887 DOI: 10.1002/wcms.70

[1]  D. Wales,et al.  Energy landscapes for shells assembled from pentagonal and hexagonal pyramids. , 2009, Physical chemistry chemical physics : PCCP.

[2]  Jinlong Yang,et al.  Novel lattice-searching method for modeling the optimal strain-free close-packed isomers of clusters. , 2007, The journal of physical chemistry. A.

[3]  Andrea Grosso,et al.  Solving molecular distance geometry problems by global optimization algorithms , 2009, Comput. Optim. Appl..

[4]  H. Rabitz,et al.  Control of quantum phenomena: past, present and future , 2009, 0912.5121.

[5]  H. Maarten Vinkers,et al.  An ant algorithm for the conformational analysis of flexible molecules , 2007, J. Comput. Chem..

[6]  Ajit J. Thakkar,et al.  Improved minima-hopping. TIP4P water clusters, (H2O)n with n⩽37 , 2009 .

[7]  Hiroshi Takeuchi,et al.  Novel Method for Geometry Optimization of Molecular Clusters: Application to Benzene Clusters , 2007, J. Chem. Inf. Model..

[8]  Harjinder Singh,et al.  Genetic algorithm optimization of laser pulses for molecular quantum state excitation. , 2010, The Journal of chemical physics.

[9]  R. de Vivie-Riedle,et al.  Modified ant-colony-optimization algorithm as an alternative to genetic algorithms , 2009 .

[10]  Youdong Lin,et al.  Deterministic global optimization of molecular structures using interval analysis , 2005, J. Comput. Chem..

[11]  M. Tremayne,et al.  Combined optimization using Cultural and Differential Evolution: application to crystal structure solution from powder diffraction data. , 2006, Chemical communications.

[12]  Xueguang Shao,et al.  A dynamic lattice searching method for fast optimization of Lennard–Jones clusters , 2004, J. Comput. Chem..

[13]  L. Ojamäe,et al.  A theoretical study of water equilibria: the cluster distribution versus temperature and pressure for (H2O)n, n = 1-60, and ice. , 2009, The Journal of chemical physics.

[14]  K. Harris,et al.  Advantages of a redefinition of variable-space in direct-space structure solution from powder x-ray diffraction data. , 2007, Chemphyschem : a European journal of chemical physics and physical chemistry.

[15]  Qunfeng Dong,et al.  A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances , 2002, J. Glob. Optim..

[16]  A. Neumaier Complete search in continuous global optimization and constraint satisfaction , 2004, Acta Numerica.

[17]  J. Doye,et al.  Structural transitions and global minima of sodium chloride clusters , 1998, cond-mat/9801152.

[18]  Sarah L Price,et al.  From crystal structure prediction to polymorph prediction: interpreting the crystal energy landscape. , 2008, Physical chemistry chemical physics : PCCP.

[19]  Yehuda Zeiri,et al.  Application of genetic algorithm to the calculation of bound states and local density approximations , 1995 .

[20]  Bicai Pan,et al.  Structures of medium-sized silicon clusters , 1998, Nature.

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

[22]  Gustav Gerber,et al.  Femtosecond quantum control of molecular dynamics in the condensed phase. , 2007, Physical chemistry chemical physics : PCCP.

[23]  J. C. Schön,et al.  Ab initio energy landscape of LiF clusters. , 2010, The Journal of chemical physics.

[24]  Andrea Grosso,et al.  An experimental analysis of a population based approach for global optimization , 2007, Comput. Optim. Appl..

[25]  Ignacio E. Grossmann,et al.  Part II. Future perspective on optimization , 2004, Comput. Chem. Eng..

[26]  Iain G. Johnston,et al.  Modelling the self-assembly of virus capsids , 2009, Journal of physics. Condensed matter : an Institute of Physics journal.

[27]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[28]  Bernd Hartke,et al.  Towards protein folding with evolutionary techniques , 2005, J. Comput. Chem..

[29]  A. Schug,et al.  Energy landscape paving simulations of the trp-cage protein. , 2005, The Journal of chemical physics.

[30]  Fedor N. Novikov,et al.  Molecular docking: theoretical background, practical applications and perspectives , 2009 .

[31]  Bernardetta Addis,et al.  A new class of test functions for global optimization , 2007, J. Glob. Optim..

[32]  C. Pantelides,et al.  Ab initio crystal structure prediction. II. Flexible molecules , 2007 .

[33]  Jonathan P K Doye,et al.  Preferential attachment during the evolution of a potential energy landscape. , 2007, The Journal of chemical physics.

[34]  The use of quantum molecular calculations to guide a genetic algorithm: a way to search for new chemistry. , 2007, Chemistry.

[35]  C. Floudas,et al.  A global optimization approach for Lennard‐Jones microclusters , 1992 .

[36]  Y. Gogotsi,et al.  Characterization of large vacancy clusters in diamond from a generational algorithm using tight binding density functional theory. , 2010, Physical Chemistry, Chemical Physics - PCCP.

[37]  Bernd Hartke Global geometry optimization of small silicon clusters at the level of density functional theory , 1998 .

[38]  Jörg Lässig,et al.  Threshold-selecting strategy for best possible ground state detection with genetic algorithms. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  R. Johnston Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries , 2003 .

[40]  G. Day,et al.  Pseudoracemic amino acid complexes: blind predictions for flexible two-component crystals. , 2010, Physical Chemistry, Chemical Physics - PCCP.

[41]  C. Adjiman,et al.  Global optimization for clusters of flexible molecules—solvent–solute interaction energy calculations , 2002 .

[42]  Hiroshi Takeuchi,et al.  Clever and Efficient Method for Searching Optimal Geometries of Lennard-Jones Clusters , 2006, J. Chem. Inf. Model..

[43]  D. Wolpert,et al.  Femtosecond quantum control of molecular bond formation , 2010, Proceedings of the National Academy of Sciences.

[44]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[45]  K. Ho,et al.  Global Optimization of 2-Dimensional Nanoscale Structures: A Brief Review , 2009 .

[46]  Stefan Goedecker,et al.  The performance of minima hopping and evolutionary algorithms for cluster structure prediction. , 2009, The Journal of chemical physics.

[47]  R. Berry Energy landscapes: topographies, interparticle forces and dynamics, and how they are related , 2010 .

[48]  Yan Feng,et al.  Funnel hopping: Searching the cluster potential energy surface over the funnels. , 2009, The Journal of chemical physics.

[49]  W. Cai,et al.  A conformational analysis method for understanding the energy landscapes of clusters. , 2007, Chemphyschem : a European journal of chemical physics and physical chemistry.

[50]  C. V. Ciobanu,et al.  Finding the reconstructions of semiconductor surfaces via a genetic algorithm [rapid communication] , 2004 .

[51]  H. Scheraga,et al.  Global optimization of clusters, crystals, and biomolecules. , 1999, Science.

[52]  Bernd Hartke,et al.  Global cluster geometry optimization by a phenotype algorithm with Niches: Location of elusive minima, and low-order scaling with cluster size , 1999, J. Comput. Chem..

[53]  B. Fingerhut,et al.  The detailed balance limit of photochemical energy conversion. , 2010, Physical chemistry chemical physics : PCCP.

[54]  Roy L. Johnston,et al.  Applications of Evolutionary Computation in Chemistry , 2004 .

[55]  Johannes M. Dieterich,et al.  OGOLEM: Global cluster structure optimisation for arbitrary mixtures of flexible molecules. A multiscaling, object-oriented approach , 2010 .

[56]  Ignacio E. Grossmann,et al.  Retrospective on optimization , 2004, Comput. Chem. Eng..

[57]  John L. Klepeis,et al.  Free energy calculations for peptides via deterministic global optimization , 1999 .

[58]  Roy L Johnston,et al.  Theoretical study of Cu(38-n)Au(n) clusters using a combined empirical potential-density functional approach. , 2009, Physical chemistry chemical physics : PCCP.

[59]  F. Gräter,et al.  Glycosylation enhances peptide hydrophobic collapse by impairing solvation. , 2010, Chemphyschem : a European journal of chemical physics and physical chemistry.

[60]  B. Hartke Global geometry optimization of clusters using genetic algorithms , 1993 .

[61]  A. Alexandrova H·(H2O)n clusters: microsolvation of the hydrogen atom via molecular ab initio gradient embedded genetic algorithm (GEGA). , 2010, The journal of physical chemistry. A.

[62]  F. Calvo,et al.  Composition-induced structural transitions in mixed rare-gas clusters , 2004 .

[63]  Johannes M. Dieterich,et al.  Design of optimally switchable molecules by genetic algorithms. , 2011, Physical chemistry chemical physics : PCCP.

[64]  Bernd Engels,et al.  Tabu search based strategies for conformational search. , 2009, The journal of physical chemistry. A.

[65]  A. K. Bakhshi,et al.  Molecular designing of novel ternary copolymers of donor-acceptor polymers using genetic algorithm , 2010, CP 2010.

[66]  Donald G. Truhlar,et al.  Parameterization of NDDO wavefunctions using genetic algorithms. An evolutionary approach to parameterizing potential energy surfaces and direct dynamics calculations for organic reactions , 1995 .

[67]  Arnold Neumaier,et al.  Molecular Modeling of Proteins and Mathematical Prediction of Protein Structure , 1997, SIAM Rev..

[68]  A. Shvartsburg,et al.  Statistical evaluation of the big bang search algorithm , 2006 .

[69]  Dusan P Djurdjevic,et al.  Ab initio protein fold prediction using evolutionary algorithms: Influence of design and control parameters on performance , 2006, J. Comput. Chem..

[70]  Tamás Vinkó,et al.  A comparison of complete global optimization solvers , 2005, Math. Program..

[71]  S. Woodley,et al.  Modelling nano-clusters and nucleation. , 2010, Physical chemistry chemical physics : PCCP.

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

[73]  Bernd Hartke,et al.  Global and local optimization of auxiliary basis sets for RI-MP2 calculations , 2004 .

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

[75]  D. Wales Energy Landscapes by David Wales , 2004 .

[76]  Hiroshi Takeuchi Development of an Efficient Geometry Optimization Method for Water Clusters , 2008, J. Chem. Inf. Model..

[77]  Fabio Schoen,et al.  Dissimilarity measures for population-based global optimization algorithms , 2010, Comput. Optim. Appl..

[78]  Johannes M. Dieterich,et al.  Composition‐induced structural transitions in mixed Lennard‐Jones clusters: Global reparametrization and optimization , 2011, J. Comput. Chem..

[79]  Aron Walsh,et al.  On the problem of cluster structure diversity and the value of data mining. , 2010, Physical chemistry chemical physics : PCCP.

[80]  Leo Liberti,et al.  Double variable neighbourhood search with smoothing for the molecular distance geometry problem , 2009, J. Glob. Optim..

[81]  David J. Wales,et al.  Free energy landscapes of model peptides and proteins , 2003 .

[82]  Constantinos C. Pantelides,et al.  Ab initio crystal structure prediction—I. Rigid molecules , 2005, J. Comput. Chem..

[83]  Christodoulos A. Floudas,et al.  A review of recent advances in global optimization , 2009, J. Glob. Optim..

[84]  S. Goedecker Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems. , 2004, The Journal of chemical physics.

[85]  Junmei Wang,et al.  Automatic parameterization of force field by systematic search and genetic algorithms , 2001, J. Comput. Chem..

[86]  W. Paszkowicz Genetic Algorithms, a Nature-Inspired Tool: Survey of Applications in Materials Science and Related Fields , 2009 .

[87]  Xin Yang,et al.  Structure of the Na(x)Cl(x+1) (-) (x=1-4) clusters via ab initio genetic algorithm and photoelectron spectroscopy. , 2004, The Journal of chemical physics.

[88]  Sharon C. Glotzer,et al.  A comparison of new methods for generating energy-minimizing configurations of patchy particles , 2009 .

[89]  Gleb Beliakov,et al.  Challenges of continuous global optimization in molecular structure prediction , 2007, Eur. J. Oper. Res..

[90]  Wei Qin,et al.  Appearance of bulk-like motifs in Si, Ge, and Al clusters. , 2010, Physical chemistry chemical physics : PCCP.

[91]  Marc Hou,et al.  Calculation of binary and ternary metallic immiscible clusters with icosahedral structures , 2008 .

[92]  Jordan A. Ramilowski,et al.  Computation of nodal surfaces in fixed-node diffusion Monte Carlo calculations using a genetic algorithm. , 2010, Physical chemistry chemical physics : PCCP.

[93]  Yan Feng,et al.  Putative global minimum structures of Morse clusters as a function of the range of the potential: 161 < or = N < or = 240. , 2009, The journal of physical chemistry. A.