Dynamical study of metallic clusters using the statistical method of time series clustering

Lai, S. K.; Lin, Yuting; Hsu, Po-Jen; Cheong, Siew Ann

doi:10.1016/j.cpc.2010.12.047

Cited by 6 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In cluster melting, instead of abruptly changing as in bulk systems, the width of the peak broadens in specific heat. This broadening has been widely discussed from the perspectives of energy, vibration spectrum (e.g., velocity autocorrelation function or instantaneous normal mode), and structural order parameters (e.g., atomic bond‐length fluctuation or common neighbor analysis). These findings indicate that shape differentiates clusters from bulk systems.…”

Section: Resultsmentioning

confidence: 99%

A new perspective of shape recognition to discover the phase transition of finite‐size clusters

Hsu

2014

J Comput Chem

View full text Add to dashboard Cite

An ultrafast shape-recognition technique was used to analyze the phase transition of finite-size clusters, which, according to our research, has not yet been accomplished. The shape of clusters is the unique property that distinguishes clusters from bulk systems and is comprehensive and natural for structural analysis. In this study, an isothermal molecular dynamics simulation was performed to generate a structural database for shape recognition of Ag-Cu metallic clusters using empirical many-body potential. The probability contour of the shape similarity exhibits the characteristics of both the specific heat and Lindemann index (bond-length fluctuation) of clusters. Moreover, our implementation of the substructure to the probability of shapes provides a detailed observation of the atom/shell-resolved analysis, and the behaviors of the clusters were reconstructed based on the statistical information. The method is efficient, flexible, and applicable in any type of finite-size system, including polymers and nanostructures.

show abstract

Section: Resultsmentioning

confidence: 99%

A new perspective of shape recognition to discover the phase transition of finite‐size clusters

Hsu

2014

J Comput Chem

View full text Add to dashboard Cite

show abstract

“…In this study, we introduce a new measure of cross correlations between financial stocks, which we call the digital cross correlation (DC) [28]. The digital correlation measures how frequently P i and P j move together, ignoring the magnitude of the price movements.…”

Section: Linear and Digital Cross Correlationsmentioning

confidence: 99%

The Chinese Correction of February 2007: How financial hierarchies change in a market crash

Teh

Goo

Lian

et al. 2015

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

“…For example, the Pearson or linear cross correlation, or the Spearman or rank cross correlation, each has its own advantages and disadvantages. In this study, we use the digital cross correlation (DCC) previously defined by Lai et al 42 in their study of the melting transition temperature of metallic clusters. In their calculations, they pointed out that the DCC is less biased towards small deviations, computationally inexpensive, and can be evaluated repeatedly for a long set of MD simulation data.…”

Section: Time Series Correlation Analysismentioning

confidence: 99%

“…More importantly, velocity fluctuations of various atoms in the protein molecule also become correlated due to the same bond and steric constraints. Spatial fingerprints consisting of different sets of strongly correlated atoms in different conformations can be discovered as well using the time series clustering or correlation analysis 42 (Sec. II D).…”

Section: Introductionmentioning

confidence: 99%

Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

Hsu

Cheong

Lai

2014

The Journal of Chemical Physics

View full text Add to dashboard Cite

Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with α helix and β hairpin, but also the important role played by weaker correlations in such protein folding dynamics.

show abstract

Dynamical study of metallic clusters using the statistical method of time series clustering

Cited by 6 publications

References 31 publications

A new perspective of shape recognition to discover the phase transition of finite‐size clusters

A new perspective of shape recognition to discover the phase transition of finite‐size clusters

The Chinese Correction of February 2007: How financial hierarchies change in a market crash

Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

Contact Info

Product

Resources

About