Understanding the thermodynamic stability and metastability of materials can help us to, for
example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to …

We review state-of-the-art Monte Carlo (MC) techniques for computing fluid coexistence
properties (Gibbs simulations) and adsorption simulations in nanoporous materials such as …

We review the materials science applications of the nested sampling (NS) method, which
was originally conceived for calculating the evidence in Bayesian inference. We describe …

We extend the nested sampling algorithm to simulate materials under periodic boundary
and constant pressure conditions, and show how it can be used to determine the complete …

Prediction of thermophysical properties from molecular principles requires accurate potential
energy surfaces (PES). We present a widely-applicable method to produce first-principles …

We introduce a novel transition path (TPS) sampling scheme employing nested sampling.
Analogous to how nested sampling explores the entire configurational phase space for …

Nested sampling has emerged as a powerful sampling technique to directly compute the
partition function of a realistic molecular system. However, it does so with a non-standard …

The recently introduced nested sampling algorithm allows the direct and efficient calculation
of the partition function of atomistic systems. We demonstrate its applicability to condensed …

The key problem of statistical physics standing over one hundred years is how to exactly
calculate the partition function (or free energy), which severely hinders the theory to be …

We present a method based on kinetic Monte Carlo (kMC) to determine the chemical
potential, Helmholtz free energy and entropy of a fluid within the course of a simulation. The …