arXiv subjects

Riccardo Ferrando

Publications and source records attributed to Riccardo Ferrando.

14 recordsLinked to original sources

arXiv2026

Geometry-controlled competition between axis centering and detwinning in fivefold-twinned gold nanoparticles

Fivefold-twinned metal nanoparticles host a central wedge disclination that strongly influences their mechanical and catalytic properties. Yet the atomistic mechanisms governing the stability, migration, and annihilation of this topological defect remain incompletely understood. Here we present a systematic molecular dynamics study of gold Marks decahedra in which the fivefold axis is artificially brought close to the surface by controlled geometric modifications. By generating concave and convex morphologies with varying axis depth, we uncover a geometry-controlled competition between axis centering and detwinning. Concave geometries promote surface diffusion that restores fivefold symmetry, either by recentering the original disclination or by nucleating a new subsurface axis through collective atomic rearrangements. In contrast, convex structures with a shallow axis undergo rapid detwinning within the first nanoseconds via surface glide, leading to single-twin or fully FCC configurations. Remarkably, positioning the axis just two atomic layers beneath the surface suppresses detwinning and restores stability. Our results demonstrate that surface curvature and defect depth critically regulate disclination mobility and twin stability, providing a mechanistic framework to understand the structural evolution of multi-twinned nanoparticles and to guide the controlled design of defect-engineered nanomaterials.

cond-mat.mes-hall↗

arXiv2026

Structural Chart of Copper-Silver Nanoalloys through machine learning

Nanoalloys (or alloy nanoparticles) are an important class of materials that are promising for their functional properties. However, designing synthesis protocols to control their structure and chemical ordering is rather challenging. Part of this difficulty stems from the lack of information on their metastable and stable structures. Here, we develop a general computational framework to construct a structural chart of nanoalloys using 38-atom AgCu nanoalloys as a model system. Initially, the equilibrium structural distribution is sampled using parallel tempering combined with molecular dynamics (PTMD). Using a machine learning (ML) based approach, the vast number of sampled configurations are classified into various structural classes. This ML approach produces a single three-dimensional map in which all structures and compositions can be visualized and discriminated. Finally, a finite-temperature structural chart is constructed which provides information on the dominant structures across the entire range of compositions and temperatures. In addition, the structural chart reveals significant differences in thermal stability between nanoalloys and bulk alloys. The presented framework provides an effective route to compute and map the vast structural and chemical space of multicomponent nanoparticles, paving the way to the rational design of functional nanoalloys.

cond-mat.mtrl-sci↗

arXiv2024

How to manipulate nanoparticle morphology with vacancies

Stacking defects in noble metal nanoparticles significantly impact their optical, catalytic, and electrical properties. While some mechanisms behind their formation have been studied, the ability to deliberately manipulate nanoparticle bulk morphology remains largely unexplored. In this work, we introduce a pioneering mechanism - vacancy-driven twinning - that enables the transformation of face-centered cubic (fcc) gold into locally hexagonal close-packed (hcp) structures. This innovative approach, demonstrated through computational simulations, facilitates the creation of realistic , randomly multi-twinned nanoparticle models. By employing a recently developed multidomain X-ray diffraction method (MDXRD), we quantitatively assess the degree of twinning. It is a crucial step in transferring theoretical studies into practical applications. Our work aims to develop tools for modifying and controlling the bulk structure of fcc nanoparticles

cond-mat.mtrl-sci↗

arXiv2024

Inherent structural descriptors via machine learning

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.

physics.comp-ph↗

arXiv2024

General theory for packing icosahedral shells into multi-component aggregates

Multi-component aggregates are being intensively researched in various fields because of their highly tunable properties and wide applications. Due to the complex configurational space of these systems, research would greatly benefit from a general theoretical framework for the prediction of stable structures, which, however, is largely incomplete at present. Here we propose a general theory for the construction of multi-component icosahedral structures by assembling concentric shells of different chiral and achiral types, consisting of particles of different sizes. By mapping shell sequences into paths in the hexagonal lattice, we establish simple and general rules for designing a wide variety of magic icosahedral structures, and we evaluate the optimal size-mismatch between particles in the different shells. The predictions of our design strategy are confirmed by molecular dynamics simulations and density functional theory calculations for several multi-component atomic clusters and nanoparticles.

physics.atm-clus↗

arXiv2023

Structural transformations in Cu, Ag, and Au metal nanoclusters

Finite-temperature structures of Cu, Ag, and Au metal nanoclusters are calculated in the entire temperature range from 0 K to melting using a computational methodology that we proposed recently [Settem \emph{et al.}, Nanoscale, 2022, 14, 939]. In this method, Harmonic Superposition Approximation (HSA) and Parallel Tempering Molecular Dynamics (PTMD) are combined in a complementary manner. HSA is accurate at low temperatures and fails at higher temperatures. PTMD, on the other hand, effectively samples the high temperature region and melting. This method is used to study the size- and system-dependent competition between various structural motifs of Cu, Ag, and Au nanoclusters in the size range 1 to 2 nm. Results show that there are mainly three types of structural changes in metal nanoclusters depending on whether a solid-solid transformation occurs. In the first type, global minimum is the dominant motif in the entire temperature range. In contrast, when a solid-solid transformation occurs, the global minimum transforms either completely to a different motif or partially resulting in a co-existence of multiple motifs. Finally, nanocluster structures are analyzed to highlight the system-specific differences across the three metals.

cond-mat.mtrl-sci↗

arXiv2023

Charting nanocluster structures via convolutional neural networks

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes.

cond-mat.mtrl-sci↗

arXiv2021

Tempering of Au nanoclusters: capturing the temperature-dependent competition among structural motifs

A computational approach to determine the equilibrium structures of nanoclusters in the whole temperature range from 0 K to melting is developed. Our approach relies on Parallel Tempering Molecular Dynamics (PTMD) simulations complemented by Harmonic Superposition Approximation (HSA) calculations and global optimization searches, thus combining the accuracy of global optimization and HSA in describing the low-energy part of configuration space, together with the PTDM thorough sampling of high-energy configurations. This combined methodology is shown to be instrumental towards revealing the temperature-dependent structural motifs in Au nanoclusters of sizes 90, 147, and 201 atoms. The reported phenomenology is particularly rich, displaying a size- and temperature-dependent competition between the global energy minimum and other structural motifs. In the case of Au$_{90}$ and Au$_{147}$, the global minimum is also the dominant structure at finite temperatures. In contrast, the Au$_{201}$ cluster undergoes a solid-solid transformation at low temperature (< 200 K). Results indicate that PTMD and HSA very well agree at intermediate temperatures, between 300 and 400 K. For higher temperatures, PTMD gives an accurate description of equilibrium, while HSA fails in describing the melting range. On the other hand, HSA is more efficient in catching low-temperature structural transitions. Finally, we describe the elusive structures close to the melting region which can present complex and defective geometries, that are otherwise difficult to characterize through experimental imaging.

physics.atm-clus↗

arXiv2019

Anionic nanoparticle-lipid membrane interactions: the protonation of anionic ligands at the membrane surface reduces membrane disruption

Monolayer-protected gold nanoparticles (Au NPs) are promising biomedical tools with applications to diagnosis and therapy, thanks to their biocompatibility and versatility. Here we show how the NP surface functionalization can drive the mechanism of interaction with lipid membranes. In particular, we show that the spontaneous protonation of anionic carboxylic groups on the NP surface can make the NP-membrane interaction faster and less disruptive.

q-bio.BM↗

arXiv2017

Local Enhancement of lipid membrane permeability induced by irradiated gold nanoparticles

Photothermal therapies are based on the optical excitation of plasmonic nanoparticles in the biological environment. The effects of the irradiation on the biological medium depend critically on the heat transfer process at the nanoparticle interface, on the temperature reached by the tissues as well as on the spatial extent of temperature gradients. Unfortunately, both the temperature and its biological effects are difficult to be probed experimentally at the molecular scale. Here, we approach this problem using nonequilibrium molecular dynamics simulations. We focus on photoporation, a photothermal application based on the irradiation of gold nanoparticles by single, short-duration laser pulses. The nanoparticles, stably bound to cell membranes, convert the radiation into heat, inducing transient changes of membrane permeability. We make a quantitative prediction of the temperature gradient around the nanoparticle upon irradiation by typical experimental laser fluences. Water permeability is locally enhanced around the NP, in an annular region that extends only a few nm far from the nanoparticle interface. We correlate the local enhancement of permeability at the NP-lipid interface to the temperature inhomogeneities of the membrane and to the consequent availability of free volume pockets within the membrane core.

physics.comp-ph↗

arXiv2017

Au Nanoparticles in Lipid Bilayers: a Comparison between Atomistic and Coarse Grained Models

The computational study of the interaction between charged, ligand-protected metal nanoparticles and model lipid membranes has been recently addressed both at atomistic and coarse grained level. Here we compare the performance of three versions of the coarse grained Martini force field at describing the nanoparticle-membrane interaction. The three coarse-grained models differ in terms of treatment of long-range electrostatic interactions and water polarizability. The NP-membrane interaction consists in the transition from a metastable NP- membrane complex, in which the NP is only partially embedded in the membrane, to a configuration in which the NP is anchored to both membrane leaflets. All the three coarse grained models provide a description of the metastable NP-membrane complex that is consistent with that obtained using an atomistic force field. As for the anchoring transition, the polarizable- water Martini correctly describes the molecular mechanisms and the energetics of the transition. The standard version of the Martini model, instead, underestimates the free energy barriers for anchoring and does not completely capture the membrane deformations involved in the transition process.

physics.bio-ph↗

arXiv2015

Interpretation of surface diffusion data with Langevin simulations - a quantitative assessment

Diffusion studies of adsorbates moving on a surface are often analyzed using 2D Langevin simulations. These simulations are computationally cheap and offer valuable insight into the dynamics, however, they simplify the complex interactions between the substrate and adsorbate atoms, neglecting correlations in the motion of the two species. The effect of this simplification on the accuracy of observables extracted using Langevin simulations was previously unquantified. Here we report a numerical study aimed at assessing the validity of this approach. We compared experimentally accessible observables which were calculated using a Langevin simulation with those obtained from explicit molecular dynamics simulations. Our results show that within the range of parameters we explored Langevin simulations provide a good alternative for calculating the diffusion procress, i.e. the effect of correlations is too small to be observed within the numerical accuracy of this study and most likely would not have a significant effect on the interpretation of experimental data. Our comparison of the two numerical approaches also demonstrates the effect temperature dependent friction has on the calculated observables, illustrating the importance of accounting for such a temperature dependence when interpreting experimental data.

cond-mat.mtrl-sci↗

arXiv2013

Morphological instability of core-shell metallic nanoparticles

Bimetallic nanoparticles (often known as nanoalloys) with core-shell arrangement are of special interest in several applications, such as in optics, catalysis, magnetism and biomedicine. Despite wide interest in applications, the physical factors stabilizing the structures of these nanoparticles are still unclear to a great extent, especially for what concerns the relationship between geometric structure and chemical ordering pattern. Here global-optimization searches are performed in order to single out the most stable chemical ordering patterns corresponding to the most important geometric structures, for a series of weakly miscible systems, including AgCu, AgNi, AgCo and AuCo. The calculations show that (i) the overall geometric structure of the nanoalloy and the shape and placement of its inner core are strictly correlated; (ii) centered cores can be obtained in icosahedral nanoparticles but not in crystalline or decahedral ones, in which asym- metric quasi-Janus morphologies form; (iii) in icosahedral nanoparticles, when the core exceeds a critical size, a new type of morphological instability develops, making the core asymmetric and extending it to- wards the nanoparticle surface; (iv) multi-center patterns can be obtained in polyicosahedral nanoalloys. Analogies and differences between the instability of the core in icosahedral nanoalloys and the Stranski- Krastanov instability occurring in thin-film growth are discussed. All these issues are crucial for designing strategies to achieve effective coatings of the cores.

cond-mat.mes-hall↗

arXiv2008

Impact-driven effects in thin-film growth: steering and transient mobility at the Ag(110) surface

Low-energy atomic impacts on the Ag(110) surface are investigated by molecular dynamics simulations based on reliable many-body semiempirical potentials. Trajectory deflections (steering) caused by the atom-surface interaction are observed, together with impact-following, transient-mobility effects. Such processes are quantitatively analysed and their dependence on the initial kinetic energy and on the impinging direction is discussed. A clear influence of the surface anisotropy on both steering and transient mobility effects is revealed by our simulations for the simple isolated-atom case and in the submonolayer-growth regime. For the latter case, we illustrate how steering and transient mobility affect the film morphology at the nanoscale.

cond-mat.mtrl-sci↗