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

Materials discovery is increasingly being impelled by machine learning methods that rely on
pre-existing datasets. Where datasets are lacking, unbiased data generation can be …

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 …

Given the binary nature of nanoalloy systems, their properties are dependent on their size,
shape, structure, composition, and chemical ordering. When energy and entropic factors for …

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 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 …

Atomic diffusion is at the basis of chemical ordering transformations in nanoalloys.
Understanding the diffusion mechanisms at the atomic level is therefore a key issue in the …

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 …

Bimetallic nanoparticles are highly relevant for applications in, eg, catalysis, sensing, and
energy harvesting. Their properties are determined by their shape, size, and, most notably …

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