Tight-binding potentials for transition metals and alloys

Effect of composition and architecture on the thermodynamic behavior of AuCu nanoparticles

The chemical and physical properties of nanomaterials ultimately rely on their crystal structures, chemical compositions and distributions. In this paper, a series of AuCu bimetallic nanoparticles with well-defined architectures and variable compositions has been addressed to explore their thermal stability and thermally driven behavior by molecular dynamics simulations. By combination of energy and Lindemann criteria, the solid-liquid transition and its critical temperature were accurately identified. Meanwhile, atomic diffusion, bond order, and particle morphology were examined to shed light on thermodynamic evolution of the particles. Our results reveal that composition-dependent melting point of AuCu nanoparticles significantly departs from the Vegard's law prediction. Especially, chemically disordered (ordered) alloy nanoparticles exhibited markedly low (high) melting points in comparison with their unary counterparts, which should be attributed to enhancing (decreasing) atomic diffusivity in alloys. Furthermore, core-shell structures and heterostructures demonstrated a mode transition between the ordinary melting and the two-stage melting with varying Au content. AuCu alloyed nanoparticles presented the evolution tendency of chemical ordering from disorder to order before melting and then to disorder during melting. Additionally, as the temperature increases, the shape transformation was observed in AuCu nanoparticles with heterostructure or L10 structure owing to the difference in thermal expansion coefficients of elements and/or of crystalline orientations. Our findings advance the fundamental understanding on thermodynamic behavior and stability of metallic nanoparticles, offering theoretical insights for design and application of nanosized particles with tunable properties.

First Principle Study on Structural, Electronic, Magnetic, and Optical Properties of Co-Doped Middle Size Silver Clusters

The structural, electronic, magnetic, and optical properties of Co-doped 10-20-atom silver clusters are investigated by GGA/PBE via the density functional theory. The Ag-Co clusters form core-shell structures with a Co atom in the center. Co atom doping modulates electronic properties like energy gap, molecular softness, global hardness, electronegativity, and electrophilicity index. For the optical spectra of the Ag-Co clusters, the energy of their spectra overall exhibits little change with increasing numbers of atoms; the strongest peaks are roughly distributed at 3.5 eV, and the intensity of their spectra overall is strengthened. Raman and vibrational spectra reflect structural changes with Co atom addition. The addition of the Co atom alters magnetic moments of specific Ag-Co clusters, while others remain unchanged.

Evolution Mechanism of Solid-Phase Catalysts During Catalytic Growth of Single-Walled Carbon Nanotubes

Using solid nanoparticles (NPs) as catalysts is the most effective method to achieve catalytic growth of single-walled carbon nanotubes (SWCNTs) with ultrapure chirality. Until now, SWCNTs with a suitable chirality purity have not been prepared in experiments. That is, the evolution of solid NPs during the catalytic growth of SWCNTs is in contradiction with the original concept of a changeless structure. Hence, in this work, the evolution mechanism of solid cobalt NPs during the nucleation process of SWCNTs is analyzed through molecular dynamics. Similar to the experimental observations, the results show that a drastic structural fluctuation of the NPs occurs during the nucleation of SWCNTs. This structural fluctuation is caused by the fact that the elastic strain energy and surface energy of the NPs can be tuned when a carbon gradient exists between the subsurface and interior of the NP. Furthermore, such a carbon gradient can be reduced by changing the carbon feeding rate. This work not only reveals the evolution mechanism of solid catalysts during the nucleation of SWCNTs but also provides prospects for realizing solid catalysts with a changeless structure by tuning the experimental parameters.

Equilibrium structure and shape of Ag and Pt nanoparticles grown on silica surfaces: From experimental investigations to the determination of a metal-silica potential

A combination of experimental and numerical investigations on metallic silver and platinum nanoparticles deposited on silica substrates is presented, with a focus on metal-substrate interactions. Experimentally, the nanoparticles, obtained by ultra-high vacuum atom deposition, are characterized by grazing-incidence small-angle x-ray scattering and high resolution transmission electronic microscopy to determine their structure and morphology and, in particular, their aspect ratio (height/diameter), which quantifies the metal-substrate interaction, from the as-grown to equilibrium state. Numerically, the interactions between the metal and the silica species are modeled with the Lennard-Jones (12, 6) potential, with two parameters for each metal and silica species. The geometric parameters were found in the literature, while the energetic parameters were determined from our experimental measurements of the aspect ratio. The parameters are as follows: σAg-O = 0.278 nm, σAg-Si = 0.329 nm, ɛAg-O = 75 meV, and ɛAg-Si = 13 meV for Ag-silica and σPt-O = 0.273 nm, σPt-Si = 0.324 nm, ɛPt-O = 110 meV, and ɛPt-Si = 18 meV for Pt-silica. The proposed Ag-silica potential reproduces quantitatively the unexpected experimental observation of the variation of the aspect ratio for Ag nanoparticles larger than 5 nm, which has been interpreted as a consequence of the silica roughness. The nanoparticle orientation, structure, and disorder are also considered. This metal-silica potential for Ag and Pt should be helpful for further studies on pure metals as well as their alloys.

Frame-by-frame observations of structure fluctuations in single mass-selected Au clusters using aberration-corrected electron microscopy

The multi-dimensional potential energy surface (PES) of a nanoparticle, such as a bare cluster of metal atoms, controls both the structure and dynamic behaviour of the particle. These properties are the subject of numerous theoretical simulations. However, quantitative experimental measurements of critical PES parameters are needed to regulate the models employed in the theoretical work. Experimental measurements of parameters are currently few in number, while model parameters taken from bulk systems may not be suitable for nanosystems. Here we describe a new measurement methodology, in which the isomer structures of a single deposited nanocluster are obtained frame-by-frame in an aberration-corrected scanning transmission electron microscope (ac-STEM) in high angle annular dark field (HAADF) mode. Several gold clusters containing 309 ± 15 atoms were analysed individually after deposition from a mass-selected cluster source onto an amorphous carbon film. The main isomers identified are icosahedral (Ih), decahedral (Dh) and face-centred-cubic (fcc) (the bulk structure), alongside many amorphous (glassy) structures. The results, which are broadly consistent with static ac-STEM measurements of an ensemble of such clusters, open the way to dynamic measurements of many different nanoparticles of diverse sizes, shapes and compositions.

Structural Determination and Hierarchical Evolution of Transition Metal Clusters Based on an Improved Self-Adaptive Differential Evolution with Neighborhood Search Algorithm

Determining the optimal structures and clarifying the corresponding hierarchical evolution of transition metal clusters are of fundamental importance for their applications. The global optimization of clusters containing a large number of atoms, however, is a vastly challenging task encountered in many fields of physics and chemistry. In this work, a high-efficiency self-adaptive differential evolution with neighborhood search (SaNSDE) algorithm, which introduced an optimized cross-operation and an improved Basin Hopping module, was employed to search the lowest-energy structures of CoN, PtN, and FeN (N = 3-200) clusters. The performance of the SaNSDE algorithm was first evaluated by comparing our results with the parallel results collected in the Cambridge Cluster Database (CCD). Subsequently, different analytical methods were introduced to investigate the structural and energetic properties of these clusters systematically, and special attention was paid to elucidating the structural evolution with cluster size by exploring their overall shape, atomic arrangement, structural similarity, and growth pattern. By comparison with those results listed in the CCD, 13 lower-energy structures of FeN clusters were discovered. Moreover, our results reveal that the clusters of three metals had different magic numbers with superior stable structures, most of which possessed high symmetry. The structural evolution of Co, Pt, and Fe clusters could be, respectively, considered as predominantly closed-shell icosahedral, Marks decahedral, and disordered icosahedral-ring growth. Further, the formation of shell structures was discovered, and the clusters with hcp-, fcc-, and bcc-like configurations were ascertained. Nevertheless, the growth of the clusters was not simply atom-to-atom piling up on a given cluster despite gradual saturation of the coordination number toward its bulk limit. Our work identifies the general growth trends for such a wide region of cluster sizes, which would be unbearably expensive in first-principles calculations, and advances the development of global optimization algorithms for the structural prediction of clusters.

Analysis of Dynamical Peculiarities in Nanoalloys at Subsystems Level: Dynamical Degrees of Freedom, Temperature Differences, and the Chameleon Effect

A novel analysis of the dynamical behavior of nanoalloy systems, as represented by model Ni/Al 13-atom clusters, over a broad range of energies that cover the stage-wise transition of the systems from their solid-like to liquid-like state is presented. Conceptually, the analysis is rooted in partitioning the systems into judiciously chosen subsystems and characterizing the latter in terms of subsystem-specific dynamical descriptors that include dynamical degrees of freedom, root-mean-square bond-length fluctuation, and element-specific subsystem temperature. The analysis reveals a host of intriguing new peculiarities in the dynamical behavior of the Ni/Al 13-mers, among which are what we call the chameleon effect and the difference in the temperatures of the Ni and Al subsystems at high energies, a difference that strongly depends on the cluster composition and also changes with energy. These do not have an analog in pure Ni13 and Al13 and are explained in terms of the coupled effects of the difference between the masses of the Ni and Al atoms (the mass effect) and of the difference in the anharmonicity of the overall interaction potential as experienced by the Ni and Al subsystems of the clusters (the potential effect).

Comparison of Taboo Search Methods for Atomic Cluster Global Optimization with a Basin-Hopping Algorithm

The basin-hopping algorithm (BHA) allows for the efficient exploration of atomic cluster potential energy surfaces by random perturbations in configuration space, followed by energy minimizations. Here, the taboo search method is incorporated to prevent the search from revisiting recently visited regions of the search space. Two taboo search modes are implemented, one mode resets the search to random coordinates upon encountering the taboo region, while the other simply rejects any proposed move into the taboo region. These two modes are tested and compared on a variety of potential energy surfaces─several clusters where atomic interactions are described by the Lennard-Jones potential, and Au55 where a semi-empirical tight binding potential is used to describe atomic interactions. Some differences in performance between the two taboo search modes were noted for LJ38 and Au55, with the mode that rejects all hops into the taboo region performing better, offering a means to improve the efficiency of the BHA for multifunnel systems. However, both taboo search modes failed to significantly improve performance on multifunnel systems where more than two funnels were present in the system.

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.

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.

The evolution mechanism of an FeMo alloy catalyst for growth of single-walled carbon nanotubes

Adding small fractions of Mo to Fe nanoparticles (NPs) can reduce the melting point of FeMo NPs to lower than that of Fe NPs to prolong the lifetime of the alloy catalyst which in turn promotes the quality of catalytically synthesized single-walled carbon nanotubes (SWCNTs). In this study, we reveal the mechanism of the above-mentioned abnormal melting behavior by employing molecular dynamics simulations. Our results indicate that the bond length between the Fe atoms and the number of bonds between the Mo atoms play an important role in reducing the melting point of the FeMo NPs. This study provides useful insight into the evolution mechanism of the alloy catalyst for the growth of SWCNTs.

On the transferability of classical pairwise additive atomistic force field to the description of unary and multi-component systems: applications to the solidification of Al-based alloys

Multi-component and multiphasic materials are continually being developed for electronics, aircraft, automotive, and general applications. Integrated Computational Materials Engineering (ICME) is a multiple-length scale approach that greatly benefits from atomistic scale simulations to explore new alloys. Molecular Dynamics (MD) allows to perform large-scale simulations by using classical interatomic potentials. The main challenge of using such a classical approach is the transferability of the interatomic potentials from one structure to another when one aims to study multi-component systems. In this work, the reliability of Zr, Al-Cu, Al-Cr and Al-Zr-Ti force field potentials is examined. It has been found that current interatomic potentials are not completely transferable due to the structure dependence from their parameterization. Besides that, they provide an appropriate description of unary and binary systems, notably for liquids, isotropic solids, and partially isotropic compounds. For solidification purposes, it has been found that coherent primary solidification of the FCC-phase in pure Al is highly dependent on the formalism to tune interatomic interactions. For Al-Cr alloys, the icosahedral short-range ordering (ISRO) increased by adding Cr to the melts. The different steps of solidification (formation of nuclei, effective germination of the α-Al phase and end of solidification) have been related to the evolution of the ISRO. The addition of Cr in melts prevented undercooling via icosahedral-enhanced nucleation of the α-Al phase. Precipitation of primary intermetallics in hyper-peritectic Al-Cr alloys was also tested. Contrary to classical thermodynamics predictions, α-Al phase was the primary precipitate for these alloys. This implies that Cr supersaturated the α-Al phase rather than forming intermetallic phases due to the high cooling rates.

Thermally activated microstructural evolution of metallic heterophase nanoparticles: insights from molecular dynamics simulations

A crystal phase is a key factor to determine the physical and chemical properties of crystalline materials. As a new class of nanoscale structures, heterophase nanoparticles, which assemble conventional and unconventional phases, exhibit exceptional properties in comparison with their single-phase counterparts. In this work, we explored the thermodynamic stability of Au, Co, and AuCo heterophase nanoparticles with fcc and hcp phases by using molecular dynamics simulations. These heterostructured nanoparticles were continuously heated to examine their thermally activated structural evolutions. Au and Co single-phase nanoparticles were also considered for comparison. The results show that the phase transition between fcc and hcp is absent in these heterophase nanoparticles despite the existence of an unconventional phase. Although the melting of Au and Co heterophase nanoparticles is homogeneous, AuCo heterophase nanoparticles show heterogeneous melting, i.e., the Au fcc domain firstly melts, followed by the melting of the Co hcp domain, exhibiting a typical two-stage melting characteristic and resulting in the existence of a solid-core/liquid-shell structure within a considerable temperature region. Furthermore, the mutual diffusion of atoms between fcc and hcp domains is observed in the Au and Co heterophase nanoparticles. However, the unidirectional diffusion from the Au domain to the Co domain is found in the AuCo heterophase nanoparticles prior to their overall melting. This study deepens the fundamental understanding of the thermodynamic evolution of metallic heterogeneous nanoparticles and provides mechanistic and quantitative guidance for the rational design and applications of nanoscale multiphase heterostructures.

BIGDML-Towards accurate quantum machine learning force fields for materials

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene-graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.

Simulation of Epitaxial Film–Substrate Interaction Potential

The formation of the substrate surface potential based on the Lennard-Jones two-particle potential is investigated in this paper. A simple atom’s square lattice on the substrate surface is considered. The periodic potential of the substrate atoms is decomposed into a Fourier series. The amplitude ratio for different frequencies has been examined numerically. The substrate potential is approximated with high accuracy by the Frenkel–Kontorova potential at most parameter values. There is a field of parameters in which the term plays a significant role, with a period half as long as the period of the substrate atoms. The ground state of the monoatomic film is modeled on the substrate potential. The film may be in both crystalline and amorphous phases. The transition to the amorphous phase is associated with a change in the landscape of the substrate potential. There are introduced order parameters for structural phase transition in the thin film. When changing the parameters of the substrate, the order parameter experiences a jump when changing the phase of the film.

Phenomenological potentials for the refractory metals Cr, Mo and W

Cohesion in the refractory metals Cr, Mo, and W is phenomenologically described in this work via an-body energy functional with a set of physically motivated parameters that were optimized to reproduce selected experimental properties characteristic of perfect and defective crystals. The functional contains four terms accounting for the hard-core repulsion, the Thomas-Fermi kinetic energy repulsion and for contributions to the binding energy ofsanddvalence electrons. Lattice dynamics, molecular statics, and molecular dynamics calculations show that this model describes satisfactorily thermodynamic properties of the studied metals whereas, unlike other empirical approaches from the literature, predictions of phonon dispersion relations and of surface and point defect energetics reveal in fair good agreement with experiments. These results suggest that the present model is well adapted to large-scale simulations and whenever total energy calculations of thermodynamic properties are unfeasible.

Structures of Small Platinum Cluster Anions Ptn-: Experiment and Theory

The structures of platinum cluster anions Pt6--Pt13- have been investigated by trapped ion electron diffraction. Structures were assigned by comparing experimental and simulated scattering functions using candidate structures obtained by density functional theory computations, including spin-orbit coupling. We find a structural evolution from planar structures (Pt6-, Pt7-) and amorphous-like structures (Pt7--Pt9-) to structures based on distorted tetrahedra (Pt9--Pt11-). Finally, Pt12- and Pt13- are based on hcp fragments. While the structural parameters are well described by density functional theory computations for all clusters studied, the predicted lowest energy structure is found in the experiment only for Pt6-. For larger clusters, higher energy isomers are necessary to obtain a fit to the scattering data.

When mechanisms of coalescence and sintering at the nanoscale fundamentally differ: Molecular dynamics study

Employing classical isothermal molecular dynamics, we simulated coalescence of mesoscopic Au nanodroplets, containing from several thousands to several hundred thousands of atoms, and sintering of mesoscopic solid Au nanoparticles. For our atomistic simulations, we used the embedded atom method. The employed open access program large-scale atomic/molecular massively parallel simulator makes it possible to realize parallel graphical processing unit calculations. We have made a conclusion that the regularities and mechanisms of the nanodroplet coalescence (temperature is higher than the nanoparticle melting temperature) and of the solid nanoparticle sintering differ from each other. We have also concluded that the nanodroplet coalescence may be interpreted as a hydrodynamic phenomenon at the nanoscale whereas sintering of solid nanoparticles is a much more complex phenomenon related to different mechanisms, including collective rearrangements of atoms, the surface diffusion, and other types of diffusion. At the same time, collective rearrangements of atoms relate not only to the solid nanoparticle sintering but also to the nanodroplet coalescence. In general, our molecular dynamics results on sintering of Au nanoparticles consisting of 10 000–30 000 atoms agree with the Ferrando–Minnai kinetic trapping concept that was earlier confirmed in molecular dynamics experiments on Au nanoclusters consisting of about 100 atoms.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters

Global optimization of multicomponent cluster structures is considerably time-consuming due to the existence of a vast number of isomers. In this work, we proposed an improved self-adaptive differential evolution with the neighborhood search (SaNSDE) algorithm and applied it to the global optimization of bimetallic cluster structures. The cross operation was optimized, and an improved basin hopping module was introduced to enhance the searching efficiency of SaNSDE optimization. Taking (PtNi)_N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)₃₈ clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)₅₅ clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.

Exploring AuRh Nanoalloys: A Computational Perspective on the Formation and Physical Properties

We studied the formation of AuRh nanoalloys (between 20-150 atoms) in the gas phase by means of Molecular Dynamics (MD) calculations, exploring three possible formation processes: one-by-one growth, coalescence, and nanodroplets annealing. As a general trend, we recover a predominance of Rh@Au core-shell ordering over other chemical configurations. We identify new structural motifs with enhanced thermal stabilities. The physical features of those selected systems were studied at the Density Functional Theory (DFT) level, revealing profound correlations between the nanoalloys morphology and properties. Surprisingly, the arrangement of the inner Rh core seems to play a dominant role on nanoclusters' physical features like the HOMO-LUMO gap and magnetic moment. Strong charge separations are recovered within the nanoalloys suggesting the existence of charge-transfer transitions.

Molecular Dynamics Characterization of Radiosensitizing Coated Gold Nanoparticles in Aqueous Environment

Functionalized metal nanoparticles (NPs) have been proposed as promising radiosensitizing agents for more efficient radiotherapy treatment using photons and ion beams. Radiosensitizing properties of NPs may depend on many different parameters (such as size, composition, and density) of the metal core, the organic coatings, and the molecular environment. A systematic exploration of each of these parameters on the atomistic level remains a formidable and costly experimental task, but it can be addressed by means of advanced computational modeling. This paper describes a detailed computational procedure for construction and atomistic-level characterization of radiosensitizing metal NPs in explicit molecular media. The procedure is general and is extensible to many different combinations of the core, coating, and environment. As an illustrative and experimentally relevant case study, we consider nanometer-sized gold NPs coated with thiol-poly(ethylene glycol)-amine molecules of different length and surface density and solvated in water at ambient conditions. The radial distribution of different atoms in the coatings as well as distribution and structural properties of water around the coated NPs are analyzed and linked to radiosensitizing properties of the NPs. It is revealed that the structure of the coating layer on the solvated NPs depends strongly on the surface density of ligands. At surface densities below ∼3 molecules/nm² the coating represents a mixture of different conformation states, whereas elongated "brush"-like structures are formed at higher densities of ligands. The water content in denser coatings is significantly lower at distances from 1 nm up to 3 nm from the gold surface depending on the length of ligand molecules. Such dense and thick coatings may suppress the production of hydroxyl radicals by low-energy electrons emitted from the metal NPs and thus diminish their radiosensitizing properties. The presented computational framework provides precise information for a quantitative atomistic-level description of the structural properties of coated metal NPs in biologically relevant environments and so may form a basis for future developments to achieve a more realistic description of irradiation-driven chemistry effects in the vicinity of coated metal NPs.

Molecular dynamics simulation of nanofilament breakage in neuromorphic nanoparticle networks

Neuromorphic computing systems may be the future of computing and cluster-based networks are a promising architecture for the realization of these systems. The creation and dissolution of synapses between the clusters are of great importance for their function. In this work, we model the thermal breakage of a gold nanofilament located between two gold nanoparticles via molecular dynamics simulations to study on the mechanisms of neuromorphic nanoparticle-based devices. We employ simulations of Au nanowires of different lengths (20-80 Å), widths (4-8 Å) and shapes connecting two Au₁₄₁₅nanoparticles (NPs) and monitor the evolution of the system via a detailed structural identification analysis. We found that atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of wire to gradually thin and then break. Most of the system remains crystalline during this process but the center is molten. The terminal NPs increase the melting point of the NWs by fixing the middle wire and act as recrystallization areas. We report a strong dependence on the width of the NWs, but also their length and structure. These results may serve as guidelines for the realization of cluster-based neuromorphic computing systems.

Relative Populations and IR Spectra of Cu38 Cluster at Finite Temperature Based on DFT and Statistical Thermodynamics Calculations

The relative populations of Cu38 isomers depend to a great extent on the temperature. Density functional theory and nanothermodynamics can be combined to compute the geometrical optimization of isomers and their spectroscopic properties in an approximate manner. In this article, we investigate entropy-driven isomer distributions of Cu38 clusters and the effect of temperature on their IR spectra. An extensive, systematic global search is performed on the potential and free energy surfaces of Cu38 using a two-stage strategy to identify the lowest-energy structure and its low-energy neighbors. The effects of temperature on the populations and IR spectra are considered via Boltzmann factors. The computed IR spectrum of each isomer is multiplied by its corresponding Boltzmann weight at finite temperature. Then, they are summed together to produce a final temperature-dependent, Boltzmann-weighted spectrum. Our results show that the disordered structure dominates at high temperatures and the overall Boltzmann-weighted spectrum is composed of a mixture of spectra from several individual isomers.

Systematics of vibrational properties of Au nanoparticles: a Molecular Dynamics approach

This paper reports the results of a Molecular Dynamics (MD) study of the vibrational properties of spherical Au nanoparticles with a number of atoms (N) varying in the range 1985...

Anomalous Size Effect of Pt Ultrathin Nanowires on Oxygen Reduction Reaction

The classical size effect of Pt particles on oxygen reduction reaction (ORR) suggests that the activity and durability would decrease with reducing the particle size, self-limiting the effectiveness in maximizing the Pt utilization efficiency with the particle-size-reduction strategy. Herein, we discover an anomalous size effect based on Pt nanowires (NWs) with tunable diameters, where the monotonically increasing activity and durability for ORR were observed with decreasing the diameter from 2.4 to 1.1 nm. Our results reveal that the dominant role of increased compressive strain induced by decreasing the diameter of NWs in weakening the adsorption and suppressing the Pt dissolution accounts for this anomalous size effect, where the reduced low-coordinated sites on NWs, the intrinsic structural advantage, is the root. Our findings not only expand the knowledge to the classical size effect but also provide new implications to break through the size limit in the design of Pt-based ORR catalysts.

Molecular dynamics simulation study of gold nanosheet as drug delivery vehicles for anti-HIV-1 aptamers

The adsorption process of three aptamers with gold nanosheet (GNS) as a drug carrier has been investigated with the help of molecular dynamics simulations. The sequencing of the considered aptamers are as (CUUCAUUGUAACUUCUCAUAAUUUCCCGAGGCUUUUACUUUCGGGGUCCU) and (CCGGGUCGUCCCCUACGGGGACUAAAGACUGUGUCCAACCGCCCUCGCCU) for AP1 and AP2, respectively. AP3 is a muted version of AP1 in which nucleotide positions 4, 6, 18, 28 and 39 have C4A, U6G, A18G, G28A, and U39C mutations. At positions 24, and 40, a deletion mutation is seen to eliminate U24 and U40 bases. These aptamers are inhibitors for HIV-1 protease and can be candidates as potential pharmaceutics for treatment of AIDS in the future. The interactions between considered aptamers and GNS have been analyzed in detail with help of structural and energetic properties. These analyses showed that all three aptamers could well adsorb on GNS. Overall, the final results show that the adsorption of AP2 on the GNS is more favorable than other considered ones and consequently GNS can be considered as a device in order to immobilize these aptamers.

An augmented (multi-descriptor) grouping algorithm to optimize chemical ordering in nanoalloys

We propose the Augmented Grouping Approach (AugGA) and its deployment in the Augmented Grouping GO (AugGGO) scheme, for an efficient exploration of the chemical ordering (or compositional structure) of multi-component (alloyed) nanoparticles. The approach is based on a 'grouping' strategy (previously proposed for high-symmetry structures) by which the number of compositional degrees of freedom of the system is decreased by defining sets of atoms (groups, or orbits, or shells) that are constrained to be populated by the same element. Three fundamental advances are here included with respect to previous proposals: (i) groups are defined on the basis of descriptors (no point-group symmetry is assumed), (ii) bulk groups can exploit general chemical ordering patterns taken from databases, and (iii) sub-grouping is realized via a multi-descriptor strategy (here using two basic descriptors: the atomic energy and a few types of geometry patterns). The AugGGO approach is applied to two prototypical examples of binary nanoalloys: Pd-Pt and Ag-Cu, with a size between ≈500 and ≈1300 atoms, in different configurations, and the convex hull of the mixing energy as a function of composition is derived. It is shown how the three advances here proposed decisively extend the power and scope of the grouping approach: (i) making it applicable to any generic structural framework, (ii) achieving a thorough sampling of the core regions of nanoparticles, and (iii) catching exotic/unexpected chemical ordering arrangements, at a computational cost which is 1-2 orders of magnitude smaller than that of traditional Monte Carlo single-exchange techniques.

Deformation Mechanisms of FCC-Structured Metallic Nanocrystal with Incoherent Twin Boundary

Incoherent twin boundaries (ITBs) can significantly affect the mechanical properties of twin-structured metals. However, most previous studies have focused on the deformation mechanism of the coherent twin boundary (CTB), and metals with ITB-accommodated plasticity still require further investigation. In this study, deformation mechanisms of FCC-structured nanocrystal metals with ITBs were investigated using molecular dynamic (MD) simulations. We revealed that three deformation mechanisms occur in metals with ITBs. The first type of deformation was observed in Au, where the plasticity is governed by partial dislocation intersections with CTBs or reactions with each other to form Lomer–Cottrell (L–C) locks. In the second type, found in Al, the deformation is governed by reversible ITB migration. The third type of deformation, in Ni and Cu, is governed by partial dislocations emitted from the ITB or the tips of the stacking faults (SFs). The observed L–C lock formation, as well as the reversible ITB migration and partial dislocation emission from the tips of SFs, have rarely been reported before.

Irradiation-driven molecular dynamics simulation of the FEBID process for Pt(PF3)4

This paper presents a detailed computational protocol for the atomistic simulation of formation and growth of metal-containing nanostructures during focused electron beam-induced deposition (FEBID). The protocol is based upon irradiation-driven molecular dynamics (IDMD), a novel and general methodology for computer simulations of irradiation-driven transformations of complex molecular systems by means of the advanced software packages MBN Explorer and MBN Studio. Atomistic simulations performed following the formulated protocol provide valuable insights into the fundamental mechanisms of electron-induced precursor fragmentation and the related mechanism of nanostructure formation and growth using FEBID, which are essential for the further advancement of FEBID-based nanofabrication. The developed computational methodology is general and applicable to different precursor molecules, substrate types, and irradiation regimes. The methodology can also be adjusted to simulate the nanostructure formation by other nanofabrication techniques using electron beams, such as direct electron beam lithography. In the present study, the methodology is applied to the IDMD simulation of the FEBID of Pt(PF3)4, a widely studied precursor molecule, on a SiO2 surface. The simulations reveal the processes driving the initial phase of nanostructure formation during FEBID, including the nucleation of Pt atoms and the formation of small metal clusters on the surface, followed by their aggregation and the formation of dendritic platinum nanostructures. The analysis of the simulation results provides spatially resolved relative metal content, height, and growth rate of the deposits, which represents valuable reference data for the experimental characterization of the nanostructures grown by FEBID.

Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms

Understanding the size- and shape-dependent properties of platinum nanoparticles is critical for enabling the design of nanoparticle-based applications with optimal and potentially tunable functionality. Toward this goal, we evaluated nine different empirical potentials with the purpose of accurately modeling faceted platinum nanoparticles using molecular dynamics simulation. First, the potentials were evaluated by computing bulk and surface properties-surface energy, lattice constant, stiffness constants, and the equation of state-and comparing these to prior experimental measurements and quantum mechanics calculations. Then, the potentials were assessed in terms of the stability of cubic and icosahedral nanoparticles with faces in the {100} and {111} planes, respectively. Although none of the force fields predicts all the evaluated properties with perfect accuracy, one potential-the embedded atom method formalism with a specific parameter set-was identified as best able to model platinum in both bulk and nanoparticle forms.

Magnetic and vibrational properties of small chromium clusters on the Cu(111) surface

The structure and magnetic properties of small Cr clusters, Cr₃ and Cr₄, adsorbed on the Cu(111) surface have been investigated using density functional theory (DFT) calculations and their vibrational properties have been studied within calculations based on tight-binding second moment approximation interatomic interaction potentials (TBSMA). It has been shown that the magnetic ordering in the Cr clusters significantly affects their crystal structure and symmetry, which influences the vibrational modes of the clusters and nearest neighbor copper atoms. In turn, these modes select potentially possible structures of Cr₃ and Cr₄, prohibiting the lowest total energy cluster structure as dynamically unstable.

$$\hbox {Ag}_{m} \hbox {Rh}_n$$ clusters with $$m+n\le 55$$

Using a combination of genetic algorithms for the unbiased structure optimization and a Gupta many-body potential for the calculation of the energetic properties of a given structure, we determine the putative total-energy minima for all $$\hbox {Ag}_{m} \hbox {Rh}_n$$ Ag m Rh n clusters with a total number of atoms $$m+n$$ m + n up to 55. Subsequently, we use various descriptors to analyze the obtained structural and energetic properties. With the help of a similarity function, we show that the pure Ag and Rh clusters are structurally similar for sizes up to around 20 atoms. The same approach gives that the mixed clusters tend to possess a larger structural similarity with the pure Rh clusters than with the pure Ag clusters. However, for clusters with $$m\simeq n\ge 25$$ m ≃ n ≥ 25 , other structures dominate. The effective coordination numbers for the Ag and Rh atoms as well as the radial distributions of those atoms indicate that there is a tendency towards segregation with Rh atoms forming an inner part and the Ag atoms forming a shell. Only few clusters, all with a fairly large total number of atoms, are found to be particularly stable.

Efficient approaches to solutions of partition function for condensed matters

The key problem of statistical physics standing over one hundred years is how to exactly calculate the partition function (or free energy), which severely hinders the theory to be applied to predict the thermodynamic properties of condensed matters. Very recently, we developed a direct integral approach (DIA) to the solutions and achieved ultrahigh computational efficiency and precision. In the present work, the background and the limitations of DIA were examined in details, and another method with the same efficiency was established to overcome the shortage of DIA for condensed system with lower density. The two methods were demonstrated with empirical potentials for solid and liquid cooper, solid argon and C₆₀ molecules by comparing the derived internal energy or pressure with the results of vast molecular dynamics simulations, showing that the precision is about ten times higher than previous methods in a temperature range up to melting point. The ultrahigh efficiency enables the two methods to be performed with ab initio calculations and the experimental equation of state of solid copper up to ∼600 GPa was well reproduced, for the first time, from the partition function via density functional theory implemented.

How accurate for phonon models to predict the thermodynamics properties of crystals

Previous work has shown that thermodynamics properties calculated by phonon model with quasi-harmonic approximation (QHA) may differ badly from experiment in some cases. The inaccuracy was examined in the present work by comparing the results of QHA for argon and copper crystal with the ones of molecular dynamics simulations, partition functions obtained by a new method or experiment. It is shown that QHA works well for the systems of atomic volume smaller than 22 ų/atom and the accuracy gets lower and lower gradually with increasing of the atomic volume. Based on this fact, the disagreement (or agreement) between the thermodynamics properties of MgO, Si, CaO, ZrO₂ calculated in previous work by QHA and the experiments can be well understood.

The role of gold atom concentration in the formation of Cu-Au nanoparticles from the gas phase

The synthesis of bimetallic nanoparticles need to be controlled in order to obtain particles of a desired size, spatial structure, and chemical composition. In the synthesis of the Cu-Au nanoparticles studied here, nanoparticles can be obtained through either chemical or physical methods, each of which has its own drawbacks. Although it is very difficult to achieve the required target chemical composition of nanoparticles during chemical synthesis, their size can be stabilized quite well. In turn, physical synthesis methods mainly allow to maintain the required chemical composition; however, the size of the resulting particles varies significantly. To solve this issue, we studied the formation of Cu-Au nanoparticles with different chemical compositions from a gaseous medium using computer molecular dynamics (MD) simulation. The aim was to determine the effect of the concentration of gold atoms on the size and on the actual chemical composition of the formed bimetallic nanoparticles. The modeled region had a cubic shape with a face length of 1350 Bohr radii and contained a total of 91125 copper and gold atoms uniformly distributed in space. Thus, based on the results of the MD simulation, it was concluded that an increase in the percentage of gold atoms in the initial vapor phase led to a decrease in the size of the synthesized nanoparticles. In addition, it was found that clusters with a size of more than 400-500 atoms, regardless of the chemical composition of the initial vapor phase, basically corresponded to a given target composition.

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

A theoretical investigation of 38-atom CuPd clusters: the effect of potential parameterisation on structure and segregation

Understanding the structure of bimetallic clusters is increasingly important due to their emerging practical applications. Herein we investigate the structure of 38-atom CuPd clusters using a genetic algorithm with cluster energies described by the semi-empirical Gupta potential. Selected clusters are then refined with density functional theory. Three different parameterisations of the Gupta potential are used and their performance assessed to understand what features of bulk and surfaces are necessary to capture for accurate description of small clusters. Three general regions of motif stability exist; for the Pd majority clusters (Pd38 to Cu4Pd34) the truncated octahedron is most stable, while for clusters of intermediate compositions (Cu5Pd33 to Cu25Pd13) a "pancake" icosahedron is most stable, and for the Cu majority clusters (Cu26Pd12 to Cu38) again the truncated octahedron is most stable. CuPd clusters tend to segregate to a Cu-core, Pd-shell structure if possible, and at higher Cu compositions, the Pd segregates to the faces of the cluster. Using multiple parameterisations of the Gupta potential ensures the full variety of possible structures is found, and improves the search for the most stable CuPd clusters.

Incorporating charge transfer effects into a metallic empirical potential for accurate structure determination in (ZnMg)N nanoalloys

We report the results of a combined empirical potential-density functional theory (EP-DFT) study to assess the global minimum structures of free-standing zinc-magnesium nanoalloys of equiatomic composition and with up to 50 atoms. Within this approach, the approximate potential energy surface generated by an empirical potential is first sampled with unbiased basin hopping simulations, and then a selection of the isomers so identified is re-optimized at a first-principles DFT level. Bader charges calculated in a previous work [A. Lebon, A. Aguado and A. Vega, Corros. Sci., 2017, 124, 35-45] revealed a significant transfer of electrons from Mg to Zn atoms in these nanoalloys; so the main novelty in the present work is the development of an improved EP, termed Coulomb-corrected-Gupta potential, which incorporates an explicit charge-transfer correction term onto a metallic Gupta potential description. The Coulomb correction has a many-body character and is fed with parameterized values of the ab initio Bader charges. The potentials are fitted to a large training set containing DFT values of cluster energies and atomic forces, and the DFT results are used as benchmark data to assess the performance of Gupta and Coulomb-corrected-Gupta EP models. Quite surprisingly, the charge-transfer correction is found to represent only 6% of the nanoalloy binding energies, yet this quantitatively small correction has a sizable beneficial effect on the predicted relative energies of homotops. Zn-Mg bulk alloys are used as the sacrificial material in corrosion-protective coatings, and the long-term goal of our research is to disclose whether those corrosion-protected capabilities are enhanced at the nanoscale.

On the elaboration of the next generation of thermodynamic models of solid solutions

Thermodynamic models of solid solutions used in computational thermochemistry have not been modernized in recent years. With the advent of fast and cheap computers, it is nowadays possible to add, at a minimal computational cost, physical ingredients such as coordination numbers, inter-atomic distances and classical interatomic potentials to the function describing the energetics of ordered and disordered solid solutions. As we show here, the integration of these elements into a robust statistical thermodynamic model of solution establishes natural connections with other deterministic and stochastic atomistic methods such as Monte Carlo and molecular dynamics simulations. Ultimately, all these numerical approaches need to be self-consistent and generate complementary sets of numerical thermo-physical properties. The present work proposes a new formalism to define the Gibbs free energy of ordered and disordered solid solutions. It allows for a complete prediction of the thermal, volumetric and compositional dependence of the Gibbs free energy by solving a constrained minimization problem. As a proof of concept, we explore the energetic behavior of pure face-centered cubic gold as well as the AuCu L1₀ ordered solution as a function of both temperature and pressure. We finally compare these results with the average properties obtained from classical molecular dynamics simulations and explain the origin of the existing differences between the two approaches based on how the temperature is accounted for in each method.