At the nanoscale, the investigation of structural features becomes fundamental as we can
establish relationships between cluster geometries and their physicochemical properties …

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 demonstrate how the many-body potential energy landscape of carbon can be explored
with the nested sampling algorithm, allowing for the calculation of its pressure-temperature …

Predicting when phase changes occur in nanoparticles is fundamental for designing the
next generation of devices suitable for catalysis, biomedicine, optics, chemical sensing and …

We propose a methodology for the calculation of nanohardness by atomistic simulations of
nanoindentation. The methodology is enabled by machine-learning interatomic potentials …

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures
near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite …

Machine learning algorithms have recently emerged as a tool to generate force fields which
display accuracies approaching the ones of the ab-initio calculations they are trained on, but …

Nanocatalyst-by-design promises to empower the next generation of electrodes for energy
devices. However, current numerical methods consider individual and often geometrical …

The ability to design synthesis processes that are out of equilibrium has opened the
possibility of creating nanomaterials with remarkable physicochemical properties, choosing …

A non-trivial interplay rules the relationship between the structure and the chemophysical
properties of a nanoparticle. In this context, characterization experiments, molecular …