Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …

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 introduce dynamic nested sampling: a generalisation of the nested sampling algorithm
in which the number of “live points” varies to allocate samples more efficiently. In empirical …

Nested sampling (NS) computes parameter posterior distributions and makes Bayesian
model comparison computationally feasible. Its strengths are the unsupervised navigation of …

This report advances the hypothesis that multifunctional systems may be associated with
multifunnel potential and free energy landscapes, with particular focus on biomolecules. It …

This perspective focuses on conceptual and computational aspects of the potential energy
landscape framework. It has two objectives: first to summarise some key developments of …

We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt
GGNS} $, combining the state of the art in differentiable programming, Hamiltonian slice …

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 …

It is now 100 years since Lennard-Jones published his first paper introducing the now
famous potential that bears his name. It is therefore timely to reflect on the many …