Tetrahedral Clusters Stabilized by Alloying.

A family of nanoclusters of tetrahedral symmetry is proposed. These clusters consist of symmetrically truncated tetrahedra with additional hexagonal islands on the four facets of the starting tetrahedron. The islands are placed in stacking fault positions. The geometric magic numbers of these clusters are derived. Global optimization searches within an atomistic potential model of Pt-Pd show that the tetrahedral structures can be stabilized for intermediate compositions of these nanoalloys, even when they are not the most stable structures of the elemental clusters. These results are also confirmed by density functional theory calculations for the magic sizes 59, 100, and 180. A thermodynamic analysis by the harmonic superposition approximation shows that Pt-Pd tetrahedral nanoalloys can be stable even above room temperature.

Hydrogen Promotes the Growth of Platinum Pyramidal Nanocrystals by Size-Dependent Symmetry Breaking.

The growth of pyramidal platinum nanocrystals is studied by a combination of synthesis/characterization experiments and density functional theory calculations. It is shown that the growth of pyramidal shapes is due to a peculiar type of symmetry breaking, which is caused by the adsorption of hydrogen on the growing nanocrystals. Specifically, the growth of pyramidal shapes is attributed to the size-dependent adsorption energies of hydrogen atoms on {100} facets, whose growth is hindered only if they are sufficiently large. The crucial role of hydrogen adsorption is further confirmed by the absence of pyramidal nanocrystals in experiments where the reduction process does not involve hydrogen.

Interplay between interdiffusion and shape transformations in nanoalloys evolving from core-shell to intermixed structures.

Nanoalloys are often grown or synthesized in non-equilibrium configurations whose further evolution towards equilibrium can take place through complex pathways. In this work, we consider bimetallic systems with tendency towards intermixing, namely AgAu, PtPd and AuCu. We analyze their evolution starting from non-equilibrium initial configurations, such as phase-separated core@shell ones, by means of molecular dynamics (MD) simulations. These systems present some differences, since AuCu bulk alloys make ordered phases at low temperature whereas AgAu and PtPd remain in solid solution. Moreover, Cu, Au and Ag have similar cohesive energies whereas Pt is much more cohesive than Pd. We consider both truncated octahedral and icosahedral initial shapes in the size range between 2 and 3 nm. For each AB system, we consider both A@B and B@A core@shell starting configurations. The evolution is characterized by monitoring the time-dependent degree of intermixing and the evolution of the shape. The simulations are performed up to temperatures close to the melting range. The approach to thermodynamic equilibrium is monitored by MD simulations and compared with the equilibrium chemical configurations obtained by Monte Carlo simulations.

Combined atomistic simulations to explore metastability and substrate effects in Ag-Co nanoalloy systems.

The Ag/Co nanoalloy system is a model system situated energetically at the limit of stability of the core-shell chemical ordering with respect to a simple phase separation behavior. This makes the system highly susceptible to effects of the environment, such as interaction with a substrate. However, kinetic effects may also be exploited by careful atom-by-atom particle growth that allows to lock in certain out-of-equilibrium configurations, such as off-center, quasi-Janus and even Janus type particles. In this contribution, we explore to what extent out-of-equilibrium structures are due to kinetic effects and the influence of the interaction of the particles with an amorphous carbon substrate by a joint experimental and molecular dynamics study. The simulation set up performed at 300 K and 600 K mimicks the experimental growth process. The substrate deforms the particles, but has also an ordering effect on particle orientation and particle structure. In the case of growth of Ag on Co seeds, particles assume close to equilibrium quasi-Janus structures, while for the deposition of Co on Ag seeds, highly out-of-equilibrium structures with several subsurface Co clusters are obtained.

Machine Learning Assisted Clustering of Nanoparticle Structures.

We propose a scheme for the automatic separation (i.e., clustering) of data sets composed of several nanoparticle (NP) structures by means of Machine Learning techniques. These data sets originate from atomistic simulations, such as global optimizations searches and molecular dynamics simulations, which can produce large outputs that are often difficult to inspect by hand. By combining a description of NPs based on their local atomic environment with unsupervised learning algorithms, such as K-Means and Gaussian mixture model, we are able to distinguish between different structural motifs (e.g., icosahedra, decahedra, polyicosahedra, fcc fragments, twins, and so on). We show that this method is able to improve over the results obtained previously thanks to the successful implementation of a more detailed description of NPs, especially for systems showing a large variety of structures, including disordered ones.

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 [M. Settem et al., Nanoscale 14, 939 (2022)]. 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 melts. 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 1-2 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, the 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 the co-existence of multiple motifs. Finally, nanocluster structures are analyzed to highlight the system-specific differences across the three metals.

Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition.

The lowest-energy structures of AgCu nanoalloys are searched for by global optimization algorithms for sizes 100 and 200 atoms depending on composition. Even though the AgCu system is very weakly miscible in macroscopic samples, the mixing energy for these nanoalloys turns out to be clearly negative for both sizes, a result which is attributed to the stabilization of non-crystalline Cu@Ag core-shell structures at the nanoscale. The mixing energy is a quantity nowadays unknown in its functional form, so that its prediction may take advantage of machine learning techniques. A support vector regressor is then implemented to successfully predict the mixing energy of AgCu nanoalloys of both sizes. Moreover, with the help of unsupervised learning algorithms, it is shown that the automatic classification of such nanoalloys into different physically meaningful structural families is indeed possible. Finally, thanks to the harmonic superposition approximation, the temperature-dependent probabilities of such structural families are calculated.

Impurity diffusion in magic-size icosahedral clusters.

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 study of the thermodynamic behavior of these systems and, in particular, of their evolution from out-of-equilibrium chemical ordering types often obtained in the experiments. Here, the diffusion is studied in the case of a single-atom impurity of Ag or Au moving within otherwise pure magic-size icosahedral clusters of Cu or Co by means of two different computational techniques, i.e., molecular dynamics and metadynamics. Our simulations reveal unexpected diffusion pathways, in which the displacement of the impurity is coupled with the creation of vacancies in the central part of the cluster. We show that the observed mechanism is quite different from the vacancy-mediated diffusion processes identified so far, and we demonstrate that it can be related to the presence of non-homogeneous compressive stress in the inner part of the icosahedral structure.

Nanoscale Effects on Phase Separation.

Classical nucleation theory predicts that a binary system which is immiscible in the bulk should become miscible at the nanoscale when lowering its size below a critical size. Here we tackle the problem of miscibility in nanoalloys with a combination of ab initio and atomistic calculations, developing a statistical-mechanics approach for the free energy cost of forming phase-separated aggregates. We apply it to the controversial case of AuCo nanoalloys. AuCo is immiscible in the bulk, but a rich variety of nanoparticle configurations, both phase-separated and intermixed, have been obtained experimentally. Our calculations strongly point to the permanence of an equilibrium miscibility gap down to the nanoscale and to the nonexistence of a critical size below which phase separation is impossible. We show that this is due to nanoscale effects of general character, caused by the existence of preferred nucleation sites in nanoparticles, which lower the free-energy cost for phase separation with respect to bulk systems.

Shear viscosity in hard-sphere and adhesive colloidal suspensions with reverse non-equilibrium molecular dynamics.

We employ the reverse non-equilibrium molecular dynamics method (RNEMD) of Müller-Plathe [Phys. Rev. E, 1999, 59, 4894] to calculate the shear viscosity of colloidal suspensions within the stochastic rotation dynamics-molecular dynamics (SRD-MD) simulation method. We examine the influence of different coupling schemes in SRD-MD on the colloidal volume fraction ϕc dependent viscosity from the dilute limit up to ϕc = 0.3. Our results demonstrate that the RNEMD method is a robust and reliable method for calculating rheological properties of colloidal suspensions. To obtain quantitatively accurate results beyond the dilute regime, the hydrodynamic interactions between the effective fluid particles in the SRD and the MD colloidal particles must be carefully considered in the coupling scheme. We benchmark the method by comparing with the hard sphere suspension case, and then calculate relative viscosities for colloids with mutually attractive interactions. We show that the viscosity displays a sharp increase at the onset of aggregation of the colloidal particles with increasing volume fraction and attraction.

Interdiffusion and crystallization of oppositely charged colloids.

The aggregation of oppositely charged colloids, usually denoted as heteroaggregation, is often used in colloidal processing, for which a precise control of the basic mechanisms of aggregate formation is of crucial importance. A promising way to achieve a better degree of control is to guide heteroaggregation by imposing geometric constraints. Here, we consider this possibility by simulating the heteroaggregation of two oppositely charged suspensions which are initially separated and then put into contact through a planar interface. Our Brownian dynamics simulations show that this type of heteroaggregation allows the formation of mixed films whose thickness can be controlled by tuning the interactions between the particles or by changing the colloidal concentration in the initial suspensions. The dependence of the type of crystalline order in these films on these parameters is also analyzed.

Aggregation of binary colloidal suspensions on attractive walls.

The adsorption of colloidal particles from a suspension on a solid surface is of fundamental importance to many physical and biological systems. In this work, Brownian Dynamics simulations are performed to study the aggregation in a suspension of oppositely charged colloidal particles in the presence of an attractive wall. For sufficiently strong attractions, the wall alters the microstructure of the aggregates so that B2 (CsCl-type) structures are more likely obtained instead of B1 (NaCl-type) structures. The probability of forming either B1 or B2 crystallites depends also on the inverse interaction range κa. Suspensions with small κa are more likely to form B2 crystals than suspensions with larger κa, even if the energetic stability of the B2 phase decreases with decreasing κa. The mechanisms underlying this aggregation and crystallization behaviour are analyzed in detail.

Doped golden fullerene cages.

A first-principles investigation of the effect of the doping of golden cages of 32 atoms is proposed. It is shown that Ag and Cu doping affects the geometrical stability of the icosahedral fullerene Au32 cage, where Ag-doping leads to a new, low symmetric, and prolate motif while Cu-doping leads to a lump, incomplete decahedral shape. Most significantly, the HOMO-LUMO gap depends strongly on the cluster geometry while its dependence on the cluster chemical composition seems to be weaker.

Correction: Study of structures and thermodynamics of CuNi nanoalloys using a new DFT-fitted atomistic potential.

Correction for 'Study of structures and thermodynamics of CuNi nanoalloys using a new DFT-fitted atomistic potential' by Emanuele Panizon et al., Phys. Chem. Chem. Phys., 2015, DOI: 10.1039/c5cp00215j.

Study of structures and thermodynamics of CuNi nanoalloys using a new DFT-fitted atomistic potential.

Shape, stability and chemical ordering patterns of CuNi nanoalloys are studied as a function of size, composition and temperature. A new parametrization of an atomistic potential for CuNi is developed on the basis of ab initio calculations. The potential is validated against experimental bulk properties, and ab initio results for nanoalloys of sizes up to 147 atoms and for surface alloys. The potential is used to determine the chemical ordering patterns of nanoparticles with diameters of up to 3 nm and different structural motifs (decahedra, truncated octahedra and icosahedra), both in the ground state and in a wide range of temperatures. The results show that the two elements do not intermix in the ground state, but there is a disordering towards solid-solution patterns in the core starting from room temperature. This order-disorder transition presents different characteristics in the icosahedral, decahedral and fcc nanoalloys.

Calculating the free energy of transfer of small solutes into a model lipid membrane: Comparison between metadynamics and umbrella sampling.

We compare the performance of two well-established computational algorithms for the calculation of free-energy landscapes of biomolecular systems, umbrella sampling and metadynamics. We look at benchmark systems composed of polyethylene and polypropylene oligomers interacting with lipid (phosphatidylcholine) membranes, aiming at the calculation of the oligomer water-membrane free energy of transfer. We model our test systems at two different levels of description, united-atom and coarse-grained. We provide optimized parameters for the two methods at both resolutions. We devote special attention to the analysis of statistical errors in the two different methods and propose a general procedure for the error estimation in metadynamics simulations. Metadynamics and umbrella sampling yield the same estimates for the water-membrane free energy profile, but metadynamics can be more efficient, providing lower statistical uncertainties within the same simulation time.

Modeling nanoscale inhomogeneities for quantitative HAADF STEM imaging.

High-angle annular dark-field scanning transmission electron microscopy in conjunction with image simulation is an important tool to determine the structure of nanomaterials. We show that molecular dynamics calculations can be combined with multislice image simulations to account for the large effects of surface-enhanced thermal vibrations and structural relaxation on image intensities. Application to a catalytically important gold cluster shows that the image intensity is sensitive to these surface dominated effects with important implications for three-dimensional structural characterizations.