Metropolis Monte Carlo has been employed with remarkable success over the years to
simulate the dense phases of polymer systems. Owing, in particular, to the freedom it …

We show that the relative entropy, S rel≡∑ p T ln (p T/p M)⁠, provides a fundamental and
unifying framework for multiscale analysis and for inverse molecular-thermodynamic …

One of the central problems in statistical mechanics is that of finding the density of states of a
system. Knowledge of the density of states of a system is equivalent to knowledge of its …

The detailed understanding of the microscopic structure of amorphous phases of metal–
organic frameworks (MOFs) remains a widely open question: characterization of these …

We consider ways to quantify the overlap of the parts of phase space important to two
systems, labeled A and B⁠. Of interest is how much of the A-important phase space lies in …

Technologies based on water adsorption such as water harvesting from air have
tremendous potential in mitigating important global crises such as water scarcity. An …

In 1953, Metropolis et al. 1 introduced an ingenious stochastic method for sampling points in
a multidimensional space, according to a prescribed probability distribution defined on that …

We present an adaptive algorithm which optimizes the statistical-mechanical ensemble in a
generalized broad-histogram Monte Carlo simulation to maximize the system's rate of round …

A simulation method is presented that achieves a flat energy distribution by updating the
statistical temperature instead of the density of states in Wang-Landau sampling. A novel …

The Markov chain Monte Carlo method is an important tool to estimate the average
properties of systems with a very large number of accessible states. This technique is used …