Possible lower energy isomer of carbon clusters C  (n = 11, 12) via particle swarm optimization algorithm: Ab initio investigation

Shi, Lingying; Wang, Zhao-Qi; Hu, Cui-E; Chen, Yan; Zhu, Jun; Ji, Geoffrey

doi:10.1016/j.cplett.2019.02.028

Cited by 9 publications

(7 citation statements)

References 95 publications

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“…The efficiency of geometry optimization (GO) algorithm is crucial for the success in the attempts to understand the cluster science. Some of the popular GO algorithms are Genetic Algorithms (GA) (Johnston, 2003), Basin Hopping (BH) (Wales and Scheraga, 1999), Particle Swarm Optimization (PSO) (Lv et al, 2012; Shi et al, 2019), Artificial Bee Colony (ABC) (Zhang and Dolg, 2015), Simulated Annealing (Kirkpatrick et al, 1983), Threshold Algorithms (Schön et al, 1996) etc. These general GO algorithms are employed in the studies of metal clusters with varying degrees of success.…”

Section: Introductionmentioning

confidence: 99%

A Global Optimizer for Nanoclusters

2019

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We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an n-sized cluster is built from the geometries of n−1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to n using this evolutionary growth strategy. We have used our strategy on some of the well-studied clusters such as Pd, Pt, Au, and Al homometallic clusters, Ru-Pt and Au-Pt binary clusters, and Ag-Au-Pt ternary cluster. We have analyzed some of the popular parameters to characterize the clusters, such as relative energy, singlet-triplet energy difference, binding energy, second-order energy difference, and mixing energy, and compared with the reported properties.

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Section: Introductionmentioning

confidence: 99%

A Global Optimizer for Nanoclusters

2019

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“…Such features can be incorporated into the modeling methodology by integrating PSO with more accurate wave function-based electronic structure methods. In another latest report, Shi et al have explored the possible structures of C 11 and C 12 using PSO (Figure a) . C 11 had two monocyclic ring structures as local minima with similar energy values, and both were considered to be putative global minima, whereas C 12 had a single candidate for the global minimum, which was again a monocyclic ring.…”

Section: Resultsmentioning

confidence: 99%

“…Putative global minima geometries of (a) single C 10 , C 11 , and C 12 rings and (b) clusters of C 14 and C 18 rings of sizes n = 2, 4, and 10. Part (a) is adapted with permissions from refs and . Copyright Elsevier.…”

Section: Resultsmentioning

confidence: 99%

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

et al. 2020

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A detailed exploration of the energy landscape is a necessary criterion for an efficient structure prediction. Although computational power has increased many-folds in recent decades, a full analysis of the potential energy surfaces (PESs) for complex chemical systems is still computationally elusive. Locating the most stable geometry of a system from a highly complex PES necessitates the use of metaheuristic techniques. Particle swarm optimization (PSO), a nature-inspired metaheuristic technique falling under the general ambit of artificial intelligence, has proven to be a promising approach for structure prediction. Through social networking, the swarm attains a collective intelligence leading to an effective search of the PESs for the most stable configurations. The PESs associated with the noncovalent interactions involving carbon nanostructures and atomic or molecular clusters are often rugged, with a large number of local minima hindering the tracking down of the global minima. In this Perspective, we demonstrate that an amalgamation of PSO with the continuum approximation, an approximation that exploits the symmetry of carbon nanostructures, serves as a robust methodology for unraveling the interactions of Lennard-Jones clusters with carbon nanostructures. The putative global minima geometries obtained from our methodology can provide fruitful insights on the structural and energetic aspects of cluster binding on carbon nanostructures and can offer excellent initial configurations for first-principles calculations.

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“…This technique enables generation of intermediate geometries for TS calculations (e.g., QST3) (Peng and Bernhard Schlegel, 1993; Peng et al, 1996), and it can also be used to guide BH searches of specified regions of the PES along isomerization pathways between two isomers. Furthermore, by implementing structural interpolation, one creates the opportunity to incorporate other GO techniques (e.g., particle swarm optimization) (Kennedy and Eberhart, 1995; Call et al, 2007; Shi et al, 2019) and machine learning techniques (e.g., growing neural gas) (Martinez and Schulten, 1991; Fritzke, 1994) into the BH algorithm. In practice, rather than an explicit analytical approach, structure interpolation can be achieved implicitly via local optimizations with a tolerable loss of accuracy.…”

Section: Augmenting the Bh Algorithmmentioning

confidence: 99%

Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

2019

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Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful for difficult non-linear optimization problems with multiple modalities and variables. Applications of these algorithms range from characterization of molecular states in statistical physics and molecular biology to geometric packing problems. A key feature of BH is the fact that one can generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima. These results can then be utilized to gain insights into molecular dynamics and thermodynamic properties. Here we describe how one can employ concepts from unsupervised machine learning to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. Specifically, we introduce the concepts of similarity indices, hierarchical clustering, and multidimensional scaling to the BH methodology. These same machine learning techniques can be used as tools for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections). We exemplify this in two case studies: (1) assigning the infrared multiple photon dissociation spectrum of the protonated serine dimer and (2) determining the temperature-dependent collision cross-section of protonated alanine tripeptide.

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Possible lower energy isomer of carbon clusters C (n = 11, 12) via particle swarm optimization algorithm: Ab initio investigation

Cited by 9 publications

References 95 publications

A Global Optimizer for Nanoclusters

A Global Optimizer for Nanoclusters

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

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Possible lower energy isomer of carbon clusters C (n = 11, 12) via particle swarm optimization algorithm: Ab initio investigation

Cited by 9 publications

References 95 publications

A Global Optimizer for Nanoclusters

A Global Optimizer for Nanoclusters

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

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Possible lower energy isomer of carbon clusters C (n = 11, 12) via particle swarm optimization algorithm: Ab initio investigation