arXiv subjects

Francesca Baletto

Publications and source records attributed to Francesca Baletto.

18 recordsLinked to original sources

arXiv2026

'pySNOW': a Python Suite for the NanO-World

In computational materials science, numerical simulations are indispensable tools for revealing atomic-scale processes. For building blocks of the nanoworld, such as nanoparticles and nanoalloys, atomistic simulations provide detailed insight into their behaviour under diverse conditions. These simulations enable the study of formation and growth mechanisms, transport phenomena, chemophysical stability, and chemical reactions, including catalytic processes. To unravel the complex structure--property relationships that characterise nanoobjects, it is crucial to develop robust and insightful representations of their atomistic structure at global and local scales. Such descriptions enhance our understanding of nanoparticle behaviour and play a key role in guiding their rational design in silico for targeted applications.

cond-mat.mtrl-sci↗

arXiv2026

Strong light-matter interactions in hybrid polaritonic systems

Strong light-matter coupling gives rise to polaritons - hybrid excitations whose mixed photonic and matter character enables control over optical, electronic and chemical properties. This Feature Article surveys the main architectures supporting polariton formation, including photonic microcavities, plasmonic nanostructures, open cavities and metasurfaces, and outlines how inorganic semiconductors, organic aggregates and hybrid systems access strong and ultrastrong coupling. Key phenomena such as coherent dynamics, vibronic interactions, dark-state reservoirs and polariton-mediated energy and electron transport are discussed, together with the experimental and theoretical tools used to study them. We highlight examples where strong coupling modifies charge transport, energy flow and chemical reactivity, and we summarize emerging regimes, including intermediate and dark-strong coupling, that broaden the landscape of hybrid light-matter physics.

physics.optics↗

arXiv2025

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations

Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging application: predicting the mass activity of Pt nanoparticles for the electrochemical oxygen reduction reaction, estimated via a microkinetic model. Accurate models are obtained when leveraging either a nanocatalyst's structure representation accessible at the computational level, namely the surface site generalized coordination number distributions, or one accessible experimentally, namely the nanoparticle's pair distance distribution function. Building on this result, we demonstrate that our machine learning model, in tandem with Bayesian optimization, efficiently identifies the Top-10 and Top-100 most active structures out of a large pool of candidates comprising more than 50000 different structures, after probing the activity only of a few thousand structures. These findings provide a robust blueprint for accelerated theoretical and experimental identification of active nanocatalysts.

cond-mat.mes-hall↗

arXiv2025

Morphological stability of Au-metal nanosatellites

Hybrid metallic nanoalloys combining plasmonic and catalytic metals are essential for developing advanced photocatalysts. A promising design called core-satellites comprises a spherical nanogold dotted with smaller transition-metal clusters. While these nanoalloys' catalytic activity and hot-carriers generation have been extensively studied, their morphological stability remains poorly explored. Performing molecular dynamics simulations, we highlight the critical role of the transition metal in governing the morphological stability of plasmonic core-satellites. Rh satellites exhibit the highest stability, while only 27\% Pt and 16\% Pd satellites survive after 200 ns at 600K. AuPt and AuPd quickly rearrange into single spherical nanostructures. AuPt forms icosahedra with an Au outer shell due to Au's surface diffusion. AuPd favors FCC and decahedral shapes and shows the highest Au mobility and significant Pd interdiffusion. In contrast, AuRh maintains its original shape, exhibiting a slow surface diffusion of gold onto rhodium and negligible mixing.

cond-mat.mes-hall↗

arXiv2025

A rational framework to estimate the chiroptical activity of [6]Helicene Derivatives

Helicenes are a class of molecules potentially suitable in several technological applications with intrinsic structural chirality which makes them interesting scaffolds for chiroptical properties. As desirable is tuning chiroptical property by synthesis, we combine experimental optical characterization and ab-initio calculations to study how different substituents influence the optical properties of [6]helicene. We explore anchoring groups presenting a variety of sizes and chemical nature, finding that both electron withdrawing and donating groups redshift and dwindle the optical activity of the molecule. We suggest the observed dumping in transitions energy and intensity is connected to the strength of the perturbation induced by the substituent on the pi-conjugation of the aromatic rings. Such observations demonstrate how helicenes' chiroptical properties can be fine-tuned via stereochemical control of the substituents and validates a simple yet effective computational setup to model the optics of those systems.

cond-mat.mtrl-sci↗

arXiv2024

Machine-learnt potential highlights melting and freezing of aluminium nanoparticles

We investigated the complete thermodynamic cycle of aluminium nanoparticles through classical molecular dynamics simulations, spanning a wide size range from 200 atoms to 11000 atoms. The aluminium-aluminium interactions are modelled using a newly developed Bayesian Force Field (BFF) from the FLARE suite, a cutting-edge tool in our field. We discuss the database requirements to include melted nanodroplets to avoid unphysical behaviour at the phase transition. Our study provides a comprehensive understanding of structural stability up to sizes as large as $3~ 10^5$ atoms. The developed Al-BFF predicts an icosahedral stability range of up to 2000 atoms, approximately 2 nm, followed by a region of stability for decahedra, up to 25000 atoms. Beyond this size, the expected structure favours face-centred cubic (FCC) shapes. At a fixed heating/cooling rate of 100K/ns, we consistently observe a hysteresis loop, where the melting temperatures are higher than those associated with solidification. The annealing of a liquid droplet further stabilizes icosahedral structures, extending their stability range to 5000 atoms. Using a hierarchical k-means clustering, we find no evidence of surface melting but observe some mild indication of surface freezing. In any event, the liquid droplet's surface shows local structural order at all sizes.

cond-mat.mtrl-sci↗

arXiv2024

Modelling Pulsed Deposition of Nanoparticles into films

We propose a numerical tool to mimic the pulsed deposition of nanoparticles, a technique used to fabricate thin films from the deposition of nanoparticles upon a substrate. We employ such tool under different initial conditions, in particular exploring the effect of depositing an heterogeneous/homogenenous sample of nanoparticles in terms of their morphology (size and shape). We monitor how changing the nature of the building block affects the porosity and roughness of the grown nanofilms. We found a strong dependence on the size of the nanoparticles, following, in the low size regime, a growth of the porosity following a power law.

cond-mat.mes-hall↗

arXiv2023

Effects of a surrounding environment during the coalescence of AuPd nanoparticles

It is far well accepted that the morphology of nanoparticles and nanoalloys is of paramount importance to understand their properties. Furthemore, the morphology depends on the growth mechanism with coalescence generally accepted as one the most common mechanisms both in liquid and in the gas phase. Coalescence refers when two existing seeds collide and aggregate into a larger object. It is expected that the resulting aggregate shows a compact, often spherical structure, although strongly out of the equilibrium, referring to its global minimum. While the coalescence of liquid droplet is widely studied, the first stages of the coalescence between nanoseeds has attracted less interest, although important as multiple aggregation can take place. Here we simulate the coalescence of Au and Pd seeds by the Molecular Dynamics method, comparing the initial stage of the coalescence in vacuum and when there is an interacting surrounding around them. We show that the surface chemical composition of the resulting aggregate depend on the environment as well as the overall morphology.

cond-mat.mes-hall↗

arXiv2022

Data-driven simulation and characterisation of gold nanoparticle melting

The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

physics.comp-ph↗

arXiv2019

On Machine Learning Force Fields for Metallic Nanoparticles

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 are much faster to compute. The enhanced computational speed of machine learning force fields results key for modelling metallic nanoparticles, as their fluxionality and multi-funneled energy landscape needs to be sampled over long time scales. In this review, we first formally introduce the most commonly used machine learning algorithms for force field generation, briefly outlining their structure and properties. We then address the core issue of training database selection, reporting methodologies both already used and yet unused in literature. We finally report and discuss the recent literature regarding machine learning force fields to sample the energy landscape and study the catalytic activity of metallic nanoparticles.

physics.comp-ph↗

arXiv2018

Building machine learning force fields for nanoclusters

We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analysing the performance of 2-body, 3-body and many-body kernel functions on a set of 19-atom Ni cluster structures. We find that 2-body GP kernels fail to provide faithful force estimates, despite succeeding in bulk Ni systems. However, both 3- and many-body kernels predict forces within a $\sim$0.1 eV/$\textÅ$ average error even for small training datasets, and achieve high accuracy even on out-of-sample, high temperature, structures. While training and testing on the same structure always provides satisfactory accuracy, cross-testing on dissimilar structures leads to higher prediction errors, posing an extrapolation problem. This can be cured using heterogeneous training on databases that contain more than one structure, which results in a good trade-off between versatility and overall accuracy. Starting from a 3-body kernel trained this way, we build an efficient non-parametric 3-body force field that allows accurate prediction of structural properties at finite temperatures, following a newly developed scheme [Glielmo et al. PRB 97, 184307 (2018)]. We use this to assess the thermal stability of Ni$_{19}$ nanoclusters at a fractional cost of full ab initio calculations.

physics.comp-ph↗

arXiv2018

A genomic characterization of metallic nanoparticles

With a focus on platinum nanoparticles of different sizes (diameter of 1-9 nm) and shapes, we sequence their geometrical genome by recording the relative occurrence of all the non equivalent active site, classified according to the number of neighbours in their first and second coordination shell. The occurrence of sites is morphology and size dependent, with significant changes in the relative occurrence up to 9 nm. Our geometrical genome sequencing approach is immediately transferable to address the effects of the morphological polydispersivity in size-selected samples and the influence of temperature, including ionic vibrations and thermal activated processes. The proposed geometrical genome forecasts an enhancement of the catalytic reduction of molecular oxygen on stellated and anisotropic platinum twinned nanoparticles, with their shortest axes of ~2 nm, and an irreversible disruption of the Pt nanocatalyst's structure above 1000 K.

cond-mat.mes-hall↗

arXiv2018

Ethanol chemisorption on core-shell Pt-nanoparticles: an ab-initio study

By means of ab-initio calculations, we have investigated the chemisorption paroperties of ethanol onto segregating binary nanoalloys. We select nanostructures with icosahedral shape of 55 atoms with a Pt outermost layer over a M core with M=Ag,Pd,Ni. With respect to nanofilms with equivalent composition, there is an increse of the ethanol binding energy. This is not merely due to observed shortening of the Pt-O distance but depends on the nanoparticle distortion after ethanol adsorption. This geometrical distortion within the nanoparticle can be interpreted as a radial breathing, which is sensitive to the adsortion site, identified by the O-anchor point and the relative positions of the ethyl group. More interestingly, being core-dependent -larger in Pd@Pt and smaller in Ni@Pt-, it relates to an effective electron transfer from ethanol and the M-core towards the Pt-shell. On the view of this new analysis, Pd@Pt nanoalloys show the most promissing features for ethanol oxidation.

cond-mat.mtrl-sci↗

arXiv2017

Size dependent rearrangements in monometallic clusters

Morphology and its stability are essential features to address physicochemical properties of metallic nanoparticles. By means of Molecular Dynamics based simulations we show a complex dependence on the size and material of common structural mechanisms taking place in mono-metallic nanoparticles at icosahedral magic sizes. We show that the well known Lipscomb s Diamond Square Diamond mechanisms, single step screw dislocation motions of the whole cluster, take place only below a given size which is material dependent. Above that size, layer by layer dislocations and/or surface peeling are likely to happen, leading to low symmetry defected motifs. The material dependence of this critical size is similar to the crossover sizes among structural motifs, based on the ration between the bulk modulus and atomic cohesive energy.

cond-mat.mes-hall↗

arXiv2016

O$_2$ adsorption trends on small supported PtNi clusters

We present a systematic analysis of molecular oxygen (O$_2$) adsorption trends on bimetallic PtNi clusters and their monometallic counterparts supported on MgO(100), by means of periodic DFT calculations for sizes between 25 up to 58 atoms. O$_2$ adsorption was studied on a variety of inequivalent sites for different structural motifs, such as truncated octahedral (TO), cuboctahedral (CO), icosahedral (Ih) and decahedral (Dh) geometries. We found that O$_2$ prefers to bind on top of two metal atoms, parallel to the cluster, with an average chemisorption energy of 1.09 eV (PtNi), 1.07 eV (Pt) and 2.09 eV (Ni), respectively. The largest adsorption energy values are found to be along the edges between two neighbouring (111)/(111) and (111)/(100) facets; while FCC and HCP sites located on the (111) facets may show a chemisorption value lower 0.3 eV where often fast O$_2$ dissociation easily occurs. Our results show that, even though it is difficult to disentangle the geometrical and electronic effects on the oxygen molecule adsorption, there is a strong correlation between the calculated general coordination number (GCN) and the chemisorp- tion map. Finally, the inclusion of dispersion corrections (DFT-D) leads to an overall increase on the calculated adsorption energy values but with a negligible alteration on the general O$_2$ adsorption trends.

cond-mat.mtrl-sci↗

arXiv2015

Topological effects in magnetic platinum nano-particles

The magnetic properties of platinum nano-particles ranging in size from a few to up 300 atoms are investigated through first-principle calculations. It is found that the total magnetization depends strongly on the local atomic rearrangements, with an enhancement around five-fold axis. This is due to an elongation of the nearest neighbor distance together with a contraction of the 2$^{nd}$ distance, resulting in a net interatomic partial charge transfer from the atoms lying on the sub-surface layer (donor) towards the vertexes (acceptor).

cond-mat.mes-hall↗

arXiv2015

Metallic nanoparticles meet Metadynamics

We show how standard Metadynamics coupled with classical Molecular Dynamics can be successfully ap- plied to sample the configurational and free energy space of metallic and bimetallic nanopclusters via the implementation of collective variables related to the pair distance distribution function of the nanoparticle itself. As paradigmatic examples we show an application of our methodology to Ag147, Pt147 and their alloy AgshellPtcore at 1:1 and 2:1 chemical compositions. The proposed scheme is not only able to reproduce known structural transformation pathways, as the five and the six square-diamond mechanisms both in pure and core-shell nanoparticles but also to predict a new route connecting icosahedron to anti-cuboctahedron.

physics.comp-ph↗

arXiv2015

Structural stability and uniformity of magnetic Pt13 nanoparticles in NaY zeolite

Based on first-principles simulations, the structural stability and magnetic uniformity of Pt13 nanoparticles encapsulated in a NaY zeolite were investigated. Among 50 stable isomers in the gas phase, only 15 could be accommodated into a zeolite pore and severe structural rearrangements occured depending on whether the solid angle at the Pt vertex bound to the supercage was larger than 2 sr (i.e. icosahedron). When van der Waals forces were included, the global minimum was found to be a new L-shaped cubic wire that is unstable in the gas phase. The total magnetization of the encapsulated Pt13 decreases due to the stabilization of less coordinated isomers, with the majority of clusters charaterized by a total magnetization of 2 μB, while the majority of free clusters exhibit a threefold value.

cond-mat.mes-hall↗