arXiv subjects

Yanhui Liu

Publications and source records attributed to Yanhui Liu.

23 records · Page 2Linked to original sources

arXiv2013

Computational studies of the glass-forming ability of model bulk metallic glasses

Bulk metallic glasses (BMGs) are produced by rapidly thermally quenching supercooled liquid metal alloys below the glass transition temperature at rates much faster than the critical cooling rate R_c below which crystallization occurs. The glass-forming ability of BMGs increases with decreasing R_c, and thus good glass-formers possess small values of R_c. We perform molecular dynamics simulations of binary Lennard-Jones (LJ) mixtures to quantify how key parameters, such as the stoichiometry, particle size difference, attraction strength, and heat of mixing, influence the glass-formability of model BMGs. For binary LJ mixtures, we find that the best glass-forming mixtures possess atomic size ratios (small to large) less than 0.92 and stoichiometries near 50:50 by number. In addition, weaker attractive interactions between the smaller atoms facilitate glass formation, whereas negative heats of mixing (in the experimentally relevant regime) do not change R_c significantly. These studies represent a first step in the development of computational methods for quantitatively predicting glass-formability.

cond-mat.mtrl-sci↗

arXiv2009

Temperature dependence of circular DNA topological states

Circular double stranded DNA has different topological states which are defined by their linking numbers. Equilibrium distribution of linking numbers can be obtained by closing a linear DNA into a circle by ligase. Using Monte Carlo simulation, we predict the temperature dependence of the linking number distribution of small circular DNAs. Our predictions are based on flexible defect excitations resulted from local melting or unstacking of DNA base pairs. We found that the reduced bending rigidity alone can lead to measurable changes of the variance of linking number distribution of short circular DNAs. If the defect is accompanied by local unwinding, the effect becomes much more prominent. The predictions can be easily investigated in experiments, providing a new method to study the micromechanics of sharply bent DNAs and the thermal stability of specific DNA sequences. Furthermore, the predictions are directly applicable to the studies of binding of DNA distorting proteins that can locally reduce DNA rigidity, form DNA kinks, or introduce local unwinding.

q-bio.BM↗

arXiv1999

The statistical properties of the volatility of price fluctuations

We study the statistical properties of volatility---a measure of how much the market is likely to fluctuate. We estimate the volatility by the local average of the absolute price changes. We analyze (a) the S&P 500 stock index for the 13-year period Jan 1984 to Dec 1996 and (b) the market capitalizations of the largest 500 companies registered in the Trades and Quotes data base, documenting all trades for all the securities listed in the three major stock exchanges in the US for the 2-year period Jan 1994 to Dec 1995. For the S&P 500 index, the probability density function of the volatility can be fit with a log-normal form in the center. However, the asymptotic behavior is better described by a power-law distribution characterized by an exponent 1 + μ\approx 4. For individual companies, we find a power law asymptotic behavior of the probability distribution of volatility with exponent 1 + μ\approx 4, similar to the S&P 500 index. In addition, we find that the volatility distribution scales for a range of time intervals. Further, we study the correlation function of the volatility and find power law decay with long persistence for the S&P 500 index and the individual companies with a crossover at approximately 1.5 days. To quantify the power-law correlations, we apply power spectrum analysis and a recently-developed modified root-mean-square analysis, termed detrended fluctuation analysis (DFA). For the S&P 500 index, DFA estimates for the exponents characterizing the power law correlations are α_1=0.66 for short time scales (within \approx 1.5 days) and α_2=0.93 for longer time scales (up to a year). For individual companies, we find α_1=0.60 and α_2=0.74, respectively. The power spectrum gives consistent estimates of the two power-law exponents.

cond-mat.stat-mech↗

arXiv1997

Volatility distribution in the S&P500 Stock Index

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent $α\cong0.9$.

cond-mat.stat-mech↗

arXiv1997

Correlations in Economic Time Series

The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1 min intervals over the 13-year period, Jan 84 -- Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time t_\times\approx 600 min. Detrended fluctuation analysis gives exponents $α_1=0.66$ and $α_2=0.93$ for $t t_\times$ respectively. Power spectrum analysis gives corresponding exponents $β_1=0.31$ and $β_2=0.90$ for $f>f_\times$ and $f< f_\times$ respectively.

cond-mat.stat-mech↗