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

We show that approximate structural relaxation with a neural network enables orders of
magnitude faster global optimization with an evolutionary algorithm in a density functional …

We assess Gaussian process (GP) regression as a technique to model interatomic forces in
metal nanoclusters by analyzing the performance of 2-body, 3-body, and many-body kernel …

The control of structural and chemical transitions in bimetallic nanoalloys at finite
temperatures is one of the challenges for their use in advanced applications. Comparing …

Because size and shape can affect the chemo-physical properties of nanoparticles, we
extend the use of geometrical descriptors to sequence a genome of monometallic …

Predicting the morphological stability of nanoparticles is an essential step towards the
accurate modelling of their chemophysical properties. Here we investigate solid–solid …

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key
for their properties but challenging to elucidate. Recent experimental advances allow …

Metal oxide supports often play an active part in heterogeneous catalysis by moderating
both the structure and the electronic properties of the metallic catalyst particle. In order to …

Platinum nanoparticles find significant use as catalysts in industrial applications such as fuel
cells. Research into their design has focussed heavily on nanoparticle size and shape as …

We present a systematic study on the adsorption properties of molecular oxygen on Pt, Ni
and PtNi clusters previously deposited on MgO (100) by means of density functional theory …