Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

In 1989, Sir Sam Edwards made the visionary proposition to treat jammed granular materials
using a volume ensemble of equiprobable jammed states in analogy to thermal equilibrium …

Recent advances in the potential energy landscapes approach are highlighted, including
both theoretical and computational contributions. Treating the high dimensionality of …

Machine learning techniques are being increasingly used as flexible non-linear fitting and
prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as …

A key challenge of nonlinear dynamics and network science is to understand how higher-
order interactions influence collective dynamics. Although many studies have approached …

Finding parameters that minimise a loss function is at the core of many machine learning
methods. The Stochastic Gradient Descent (SGD) algorithm is widely used and delivers …

In recent years, substantial progress has been made in the modeling of organic solids.
Computer simulation has been increasingly sha** the area of new organic materials by …

We describe and analyze some Monte Carlo methods for manifolds in euclidean space
defined by equality and inequality constraints. First, we give an MCMC sampler for …

To explore basin geometry in high-dimensional dynamical systems, we consider a ring of
identical Kuramoto oscillators. Many attractors coexist in this system; each is a twisted …

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 …