https://doi.org/10.1140/epjst/e2016-60326-1
Regular Article
Comparison of Boltzmann and Gibbs entropies for the analysis of single-chain phase transitions
1 Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale), Germany
2 Faculty of Physics, Moscow State University, Moscow 119991, Russia
a e-mail: Wolfgang.Paul@physik.uni-halle.de
Received: 14 October 2016
Revised: 12 December 2016
Published online: 5 April 2017
In the last 10 years, flat histogram Monte Carlo simulations have contributed strongly to our understanding of the phase behavior of simple generic models of polymers. These simulations result in an estimate for the density of states of a model system. To connect this result with thermodynamics, one has to relate the density of states to the microcanonical entropy. In a series of publications, Dunkel, Hilbert and Hänggi argued that it would lead to a more consistent thermodynamic description of small systems, when one uses the Gibbs definition of entropy instead of the Boltzmann one. The latter is the logarithm of the density of states at a certain energy, the former is the logarithm of the integral of the density of states over all energies smaller than or equal to this energy. We will compare the predictions using these two definitions for two polymer models, a coarse-grained model of a flexible-semiflexible multiblock copolymer and a coarse-grained model of the protein poly-alanine. Additionally, it is important to note that while Monte Carlo techniques are normally concerned with the configurational energy only, the microcanonical ensemble is defined for the complete energy. We will show how taking the kinetic energy into account alters the predictions from the analysis. Finally, the microcanonical ensemble is supposed to represent a closed mechanical N-particle system. But due to Galilei invariance such a system has two additional conservation laws, in general: momentum and angular momentum. We will also show, how taking these conservation laws into account alters the results.
© EDP Sciences, Springer-Verlag, 2017
