Free energy landscape theory of glass transition and entropy

2009 ◽  
Vol 355 (10-12) ◽  
pp. 681-685 ◽  
Author(s):  
T. Odagaki ◽  
A. Yoshimori
2000 ◽  
Vol 11 (02) ◽  
pp. 301-308 ◽  
Author(s):  
NELSON A. ALVES ◽  
ULRICH H. E. HANSMANN

The free-energy landscape of two peptides is evaluated at various temperatures and an estimate for its fractal dimension at these temperatures calculated. We show that monitoring this quantity as a function of temperature allows to determine the glass transition temperature.


2006 ◽  
Vol 352 (42-49) ◽  
pp. 4843-4846 ◽  
Author(s):  
T. Odagaki ◽  
T. Yoshidome ◽  
A. Koyama ◽  
A. Yoshimori

2019 ◽  
Author(s):  
Xiaohui Wang ◽  
Zhaoxi Sun

<p>Correct calculation of the variation of free energy upon base flipping is crucial in understanding the dynamics of DNA systems. The free energy landscape along the flipping pathway gives the thermodynamic stability and the flexibility of base-paired states. Although numerous free energy simulations are performed in the base flipping cases, no theoretically rigorous nonequilibrium techniques are devised and employed to investigate the thermodynamics of base flipping. In the current work, we report a general nonequilibrium stratification scheme for efficient calculation of the free energy landscape of base flipping in DNA duplex. We carefully monitor the convergence behavior of the equilibrium sampling based free energy simulation and the nonequilibrium stratification and determine the empirical length of time blocks required for converged sampling. Comparison between the performances of equilibrium umbrella sampling and nonequilibrium stratification is given. The results show that nonequilibrium free energy simulation is able to give similar accuracy and efficiency compared with the equilibrium enhanced sampling technique in the base flipping cases. We further test a convergence criterion we previously proposed and it comes out that the convergence behavior determined by this criterion agrees with those given by the time-invariant behavior of PMF and the nonlinear dependence of standard deviation on the sample size. </p>


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