Conspectus Multimetallic nanoparticles (NPs) have highly tunable properties due to the
synergy between the different metals and the wide variety of NP structural parameters such …

Crystal structure prediction (CSP) is the problem of determining the most stable crystalline
arrangements of materials given their chemical compositions. In general, CSP …

Abstract Machine learning techniques are increasingly being employed to predict molecular
properties. Such models are often trained on large computationally derived datasets, and …

Highlights•Overview of data driven methods to compute nanoparticle stability
metrics.•Stability metrics include chemical potentials and cohesive energies of …

A new genetic algorithm has been proposed focusing on direct ab initio potential energy
surface (PES) global minima search. Besides the commonly used operators, this new …

Novel materials are being enabled by advances in synthesis techniques that achieve ever
better control over the atomic-scale structure of materials. The pace of materials …

Clusters são agregados de partículas que podem abranger propriedades bem diferentes,
dependendo de seu tamanho e composição, entre outras características, podendo se …

Computational materials discovery involves a complex interplay between physics-based
modeling, data-driven machine learning, and optimization. Despite fast-growing …

In the first part of this work, we consider small nanoparticles, aka nanoclusters, of transition
metals. Transition metal nanoclusters have been studied extensively for a wide range of …