Colloidal clusters from a global optimization perspective

Discovery of two-dimensional binary nanoparticle superlattices using global Monte Carlo optimization

Binary nanoparticle (NP) superlattices exhibit distinct collective plasmonic, magnetic, optical, and electronic properties. Here, we computationally demonstrate how fluid-fluid interfaces could be used to self-assemble binary systems of NPs into 2D superlattices when the NP species exhibit different miscibility with the fluids forming the interface. We develop a basin-hopping Monte Carlo (BHMC) algorithm tailored for interface-trapped structures to rapidly determine the ground-state configuration of NPs, allowing us to explore the repertoire of binary NP architectures formed at the interface. By varying the NP size ratio, interparticle interaction strength, and difference in NP miscibility with the two fluids, we demonstrate the assembly of an array of exquisite 2D periodic architectures, including AB-, AB₂-, and AB₃-type monolayer superlattices as well as AB-, AB₂-, A₃B₅-, and A₄B₆-type bilayer superlattices. Our results suggest that the interfacial assembly approach could be a versatile platform for fabricating 2D colloidal superlattices with tunable structure and properties.

Application of Optimization Algorithms in Clusters

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard-Jones (LJ), Born-Mayer, Gupta, Sutton-Chen, and Murrell-Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.

Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques

We propose a new methodology to study, at the density functional theory (DFT) level, the clusters resulting from the microsolvation of alkali-metal ions with rare-gas atoms. The workflow begins with a global optimization search to generate a pool of low-energy minimum structures for different cluster sizes. This is achieved by employing an analytical potential energy surface (PES) and an evolutionary algorithm (EA). The next main stage of the methodology is devoted to establish an adequate DFT approach to treat the microsolvation system, through a systematic benchmark study involving several combinations of functionals and basis sets, in order to characterize the global minimum structures of the smaller clusters. In the next stage, we apply machine learning (ML) classification algorithms to predict how the low-energy minima of the analytical PES map to the DFT ones. An early and accurate detection of likely DFT local minima is extremely important to guide the choice of the most promising low-energy minima of large clusters to be re-optimized at the DFT level of theory. In this work, the methodology was applied to the Li+Krn (n = 2-14 and 16) microsolvation clusters for which the most competitive DFT approach was found to be the B3LYP-D3/aug-pcseg-1. Additionally, the ML classifier was able to accurately predict most of the solutions to be re-optimized at the DFT level of theory, thereby greatly enhancing the efficiency of the process and allowing its applicability to larger clusters.

Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc.

Two Perturbations for Geometry Optimization of Off-lattice Bead Protein Models

Referring to the optimization algorithm previously developed for atomic clusters, the present author develops an efficient method for geometry optimization of a coarse-grained protein model expressed with two kinds of beads (hydrophilic and hydrophobic ones). In the method, two types of geometrical perturbations, center-directed bead move and one bead rotation, are used to explore new configurations and local optimizations are performed after the perturbations. The center-directed bead move is used for hydrophobic beads and the one bead rotation is performed for both hydrophobic and hydrophilic beads. The optimization method was applied to protein models consisting of 13, 20, 21, and 34 beads. The present method produced the global minima of the 13-, 21-, and 34-bead models reported in the literature and updated the lowest energies of the protein models with 20 beads. These results indicate that the present method is efficient for searching for optimal structures of proteins.

Solvation of Li+ by argon: how important are three-body forces?

A global geometry search on a new potential energy surface for Li⁺Ar_n clusters revealed that three-body interactions must be included to reproduce ab initio structures and accurate energetic features.

A Detailed Study on the Low-Energy Structures of Charged Colloidal Clusters

The target of this investigation is the systematic characterization of the low-energy structures of charged colloidal clusters that may be important to understand the self-assembling process of biomolecules. The aggregation of charged colloidal particles is governed by the attractive short-ranged Morse potential and the Yukawa repulsive tail to describe the long-range charge effect. A global optimization strategy, based on our own evolutionary algorithm, was adopted to discover the low-energy structures of colloidal clusters composed of up to 20 particles. A detailed analysis of the low-energy structures involving charged particles shows that the appearance of the Bernal spiral as the most stable motif occurs, first, at N = 6, but it is favored for larger clusters (N ≥ 13); for 6 ≤ N ≤ 12, there is a competition between the spiral (which is favored for higher charges) and more spherical-like structures. Finally, we study binary clusters composed by two sets of differently charged colloidal particles. Although a great diversity of low-energy structures is observed (especially for aggregates with one of the components in excess), the global minimum is disputed by three structural motifs depending on the composition of the cluster and, in some cases, on the range of the Morse potential.

Low-energy structures of benzene clusters with a novel accurate potential surface

The benzene-benzene (Bz-Bz) interaction is present in several chemical systems and it is known to be crucial in understanding the specificity of important biological phenomena. In this work, we propose a novel Bz-Bz analytical potential energy surface which is fine-tuned on accurate ab initio calculations in order to improve its reliability. Once the Bz-Bz interaction is modeled, an analytical function for the energy of the Bzn clusters may be obtained by summing up over all pair potentials. We apply an evolutionary algorithm (EA) to discover the lowest-energy structures of Bzn clusters (for n=2-25), and the results are compared with previous global optimization studies where different potential functions were employed. Besides the global minimum, the EA also gives the structures of other low-lying isomers ranked by the corresponding energy. Additional ab initio calculations are carried out for the low-lying isomers of Bz3 and Bz4 clusters, and the global minimum is confirmed as the most stable structure for both sizes. Finally, a detailed analysis of the low-energy isomers of the n = 13 and 19 magic-number clusters is performed. The two lowest-energy Bz13 isomers show S6 and C3 symmetry, respectively, which is compatible with the experimental results available in the literature. The Bz19 structures reported here are all non-symmetric, showing two central Bz molecules surrounded by 12 nearest-neighbor monomers in the case of the five lowest-energy structures.