Structure and vibrational spectroscopy of halide ion hydrates: a study based on genetic algorithm

Chaudhury, Pinaki; Saha, Rajendra; Bhattacharyya, S.P.

doi:10.1016/s0301-0104(01)00410-4

Cited by 18 publications

(9 citation statements)

References 17 publications

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“…This phenomenon can be described as a structural transition from ion‐centered forms to surface forms, with increasing ion size. For halogen anions201, 203, 204, 247–250 it is tempting to draw a similar conclusion, since Cl − and larger anions clearly prefer surface solvation, whereas centered structures can compete with surface structures only for the smaller F − ion. The cited sources agree, however, in ascribing this effect not just to the size of the ion but also to its polarizability (which is also related to the size).…”

Section: Exemplary Systemsmentioning

confidence: 98%

Structural Transitions in Clusters

Hartke¹

2002

Angew. Chem. Int. Ed.

130

109

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If one adds more particles to a cluster, the energetically optimal structure is neither preserved nor does it change in a continuous fashion. Instead, one finds several cluster size regions where one structural principle dominates almost without exception, and rather narrow boundary regions in‐between. The structure of the solid is usually reached only at relatively large sizes, after more than one structural transition. The occurrence of this general phenomenon of size‐dependent structural transitions does not seem to depend on the nature of the particles, it is found for atomic, molecular, homogeneous, and heterogeneous clusters alike. Clearly, it is a collective many‐body phenomenon which can in principle be calculated but not understood in a fully reductionistic manner. Actual calculations with sufficient accuracy are not feasible today, because of the enormous computational expense, even when unconventional evolutionary algorithms are employed for global geometry optimization. Therefore, simple rules for cluster structures are highly desirable. In fact, we are dealing here not just with the academic quest for linkages between cluster structure and features of the potential energy surface, but structural transitions in clusters are also of immediate relevance for many natural and industrial processes, ranging from crystal growth all the way to nanotechnology. This article provides an exemplary overview of research on this topic, from simple model systems where first qualitative explanations start to be successful, up to more realistic complex systems which are still beyond our understanding.

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Section: Exemplary Systemsmentioning

confidence: 98%

Structural Transitions in Clusters

Hartke¹

2002

Angew. Chem. Int. Ed.

130

109

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“…TIP4P-like empirical potentials can still be used to describe the intermolecular interaction and the interaction between H 2 O molecules and alkali-metal/halogen ions. In previous works, the global minima structures of Cl Ϫ1 (Br Ϫ1 , I Ϫ1 )(H 2 O) n (n ϭ 1 Ϫ 6), 149 Na ϩ (H 2 O) n (n ϭ 4 Ϫ 25), 150 M ϩ (H 2 O) n (n ϭ 4 Ϫ 22, M ϭ Na, K, Cs) 151 clusters were studied using GA optimization.…”

Section: Water Clustersmentioning

confidence: 99%

Genetic Algorithms for the Geometry Optimization of Atomic and Molecular Clusters

Zhao¹,

Xie²

2004

Jnl of Comp & Theo Nano

100

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“…Over the years GA itself has evolved from just being a computational tool to one of the most used optimization techniques for difficult real‐world problems . Although GA has been used in quantum chemistry for solving eigenvalue problems, this work is the first attempt to implement GA in the CI framework. The genetic algorithm configuration interaction (GACI) has similarities to MCCI and ACI methods.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm based configuration interaction approach

Chakraborty

Ghosh

2017

Int J of Quantum Chemistry

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A stochastic configuration interaction method based on evolutionary algorithm is designed as an affordable approximation to full configuration interaction (FCI). The algorithm comprises of initiation, propagation and termination steps, where the propagation step is performed with cloning, mutation and cross-over, taking inspiration from genetic algorithm. We have tested its accuracy in 1D Hubbard problem and a molecular system (symmetric bond breaking of water molecule). We have tested two different fitness functions based on energy of the determinants and the CI coefficients of determinants. We find that the absolute value of CI coefficients is a more suitable fitness function when combined with a fixed selection scheme.Keywords: genetic algorithm, evolutionary algorithm, configuration interaction Majority of the electronic structure methods that have been developed and used over the last few decades starts with the independent orbital approximation, i.e. the assumption that a single Slater determinant is a qualitatively correct starting point for a calculation. This qualitatively correct reference is typically corrected for dynamic correlation with post Hartree Fock (HF) methods such as Moller-Plesset perturbation theory (MP2) or coupled cluster singles and doubles (CCSD). However, the assumption of a single Slater determinant as a reference is not qualitatively correct, especially in situations where there are significant orbital degeneracies or neardegeneracies, e.g., bond breaking or di-and tri-radicals. Such systems are referred to as strongly correlated systems and the electronic correlation in these systems are referred to as static correlation, as opposed to dynamic correlation. It is important to note that we are not differentiating between true correlation due to orbital degeneracies and that required to treat proper spin symmetry (non-dynamic and static correlations). 1 Full configuration interaction (FCI) is the most rigorous method to treat correlation, both static and dynamic. However, FCI involves exact diagonalization of the full Hamiltonian in its Hilbert space and is therefore, not affordable for reasonable system sizes and basis sets. 2 Therefore, approximate methods such as CASSCF 3 and RASSCF 4 etc have been developed where only a subset of the orbital space is treated exactly to reduce the computational cost. But these methods also involve an exact diagonalization, albeit over a smaller sub-space. On the other hand, density matrix renormalization group (DMRG) 5-9 have been developed to circumvent the exact diagonalization problem and therefore, the associated exponential scaling. While DMRG has been remarkably successful in the case of pseudo-linear systems, more general 2D and 3D systems are complicated due to problems in orbital ordering. 10,11 However, there have been a) http://academic.ncl.res.in/debashree.ghosh b) Electronic mail: debashree.ghosh@gmail.com developments towards using tensor networks to alleviate this problem. 12,13 Antisymmetrized geminal power (AGP) wavefunctions ha...

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Structure and vibrational spectroscopy of halide ion hydrates: a study based on genetic algorithm

Cited by 18 publications

References 17 publications

Structural Transitions in Clusters

Structural Transitions in Clusters

Genetic Algorithms for the Geometry Optimization of Atomic and Molecular Clusters

Evolutionary algorithm based configuration interaction approach

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