Energy landscapes: Application to silicon nanoclusters and protein stabilities

Abstract

Statistical thermodynamics provides the relations between thermodynamical information and microscopic information. Energy landscapes provide the information of macroscopic phenomena, i.e. phase transitions, protein folding problems, via the statistical thermodynamics. Among energy landscapes, potential energy surfaces, which are determined by the exact positions of all particles, are useful to investigate the global minima of clusters in conjunction with the global search techniques while free energy surfaces, which depend on the additional parameters such as temperature T, have been used to study the free-energy barrier between two thermal (meta)stable states as a function of order parameters or reaction coordinates. In this work, the concept of the energy landscape is applied to nanoscaled semiconductor clusters, phase transitions, and thermal stabilities of proteins. The quantum-mechanical calculations combined with the global search techniques have been performed to unveil the structural information of low-lying silicon clusters Si_n (13 ≤ n ≤ 39) since their detail morphologies still cannot be inferred directly from experiments. Free energy surface as a function of a proper order parameter is employed for the temperature dependence of protein's thermal stability resembling the macroscopic first-order phase transition behavior.