Statistical thermodynamics of the cluster solid-liquid transition

100 Years of the Lennard-Jones Potential

It is now 100 years since Lennard-Jones published his first paper introducing the now famous potential that bears his name. It is therefore timely to reflect on the many achievements, as well as the limitations, of this potential in the theory of atomic and molecular interactions, where applications range from descriptions of intermolecular forces to molecules, clusters, and condensed matter.

Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields

We proposed a parameter-free volume element representation that satisfies the electron counting model and obtains accurate machine learning potential energy and direct force fitting of randomly perturbed hexagonal BN. Our method preserves permutational, translational, and rotational invariance and can be extended to three-dimensional systems, verified by a system of bulk Si. As a result, we obtained 0.57 meV/atom potential energy root mean squared error (RMSE) and 59 meV/Å force RMSE for perturbed bulk BN systems and 0.43 meV/atom potential energy RMSE and 36 meV/Å force RMSE for perturbed Si systems. In addition, an unbiased perturbation-based data set construction scheme is introduced and a continuous population distribution is obtained with a training data set of 4500, which is about 1 order of magnitude smaller than standard methods based on first-principles molecular dynamics simulations and saves a large amount of computing resources. General validity of our model is verified by structure optimization, molecular dynamics simulations, and extrapolations.

Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au20 nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement.

Structural dynamics of Ru clusters during nitrogen dissociation in ammonia synthesis

The dynamic evolution of catalyst structures greatly influences the reactivity, especially sub-nanometer clusters, exhibiting complex configurational fluctuation. In the present work, we study the structural dynamics of a Ru₁₉ cluster during the dissociation of N₂ and calculate the reaction free energies using ab initio molecular dynamics (AIMD). Our AIMD calculation predicts a peak-shaped reaction entropy curve due to the adsorption-induced phase transition of the Ru₁₉ cluster. The low melting points of sub-nanometer clusters make it possible to activate N₂ at low temperatures. This work demonstrates that the dynamic changes of cluster structures have a non-negligible effect on reaction free energy and offer an opportunity for achieving ammonia synthesis under mild conditions.

Prethermalization with negative specific heat

We study noncanonical relaxation in an aggregate of subsystems with negative specific heat. The Thirring instability drives the constituent subsystems towards the edges of their energy spectrum, so that the existence of a single adiabatic invariant results in structured noncanonical steady states that are spectacularly different from the grand-canonical prediction. For parameter regimes where this adiabatic invariance breaks down, the system exhibits prethermalization far away from integrability, with an unprecedented contrast between the prethermal- and thermal states.

Potential energy landscape and thermodynamic transitions of coarse-grained protein models revealed by the multicanonical generalized hybrid Monte Carlo method

In the present study, thermodynamic properties of coarse-grained protein models have been studied by an extended ensemble method. Two types of protein model were analyzed; one is categorized into a fast folder and the other into a slow folder. Both models exhibit the following thermodynamic transitions: the collapse transition between random coil states and spatially compact, but non-native states and the folding transition between the collapsed states and the folded native states. Caloric curve for the fast folder shows strong statistical ensemble dependence, while almost no ensemble dependence is found for the slow folder. Microcanonical caloric curve for the fast folder exhibits S-shaped temperature dependence on the internal energy around the collapse transition which is reminiscent of the van der Waals loop observed for the first order transition; at the transition temperature, the collapsed and random coil states coexist dynamically. The corresponding microcanonical heat capacity is found to have negative region around the transition. This kind of exotic behaviors could be utilized to distinguish fast folding proteins.

Density-functional tight-binding: basic concepts and applications to molecules and clusters

The scope of this article is to present an overview of the Density Functional based Tight Binding (DFTB) method and its applications. The paper introduces the basics of DFTB and its standard formulation up to second order. It also addresses methodological developments such as third order expansion, inclusion of non-covalent interactions, schemes to solve the self-interaction error, implementation of long-range short-range separation, treatment of excited states via the time-dependent DFTB scheme, inclusion of DFTB in hybrid high-level/low level schemes (DFT/DFTB or DFTB/MM), fragment decomposition of large systems, large scale potential energy landscape exploration with molecular dynamics in ground or excited states, non-adiabatic dynamics. A number of applications are reviewed, focusing on -(i)- the variety of systems that have been studied such as small molecules, large molecules and biomolecules, bare orfunctionalized clusters, supported or embedded systems, and -(ii)- properties and processes, such as vibrational spectroscopy, collisions, fragmentation, thermodynamics or non-adiabatic dynamics. Finally outlines and perspectives are given.

Excess thermal energy and latent heat in nanocluster collisional growth

Nanoclusters can form and grow by nanocluster-monomer collisions (condensation) and nanocluster-nanocluster collisions (coagulation). During growth, product nanoclusters have elevated thermal energies due to potential and thermal energy exchange following a collision. Even though nanocluster collisional heating may be significant and strongly size dependent, no prior theory describes this phenomenon for collisions of finite-size clusters. We derive a model to describe the excess thermal energy of collisional growth, defined as the kinetic energy increase in the product cluster, and latent heat of collisional growth, defined as the heat released to the background upon thermalization of the nonequilibrium cluster. Both quantities are composed of a temperature-independent term related to potential energy minimum differences and a size- and temperature-dependent term, which hinges upon heat capacity and energy partitioning. Example calculations using gold nanoclusters demonstrate that collisional heating can be important and strongly size dependent, particularly for reactive collisions involving nanoclusters composed of 14-20 atoms. Excessive latent heat release may have considerable implications in cluster formation and growth.

Evidencing the relationship between isomer spectra and melting: the 20- and 55-atom silver and gold cluster cases

The present work highlights the links between melting properties and structural excitation spectra of small gold and silver clusters. The heat capacity curves are computed for Ag₂₀, Au₂₀, Ag₅₅, Au₅₅ and their ions, using a parallel-tempering molecular dynamics scheme to explore the density functional based tight binding (DFTB) potential energy surfaces and the multiple histogram method. It is found that clusters having very symmetric lowest energy structures (Au₂₀, Ag₅₅ and their ions) present sharp or relatively sharp solid-to-liquid transitions and large melting temperatures, important structural excitation energies and a discrete isomer spectrum. Opposite trends are observed for less ordered clusters (Ag₂₀, Au₅₅ and their ions). Regarding the structural evolution with temperature, very symmetric clusters exhibit minor evolution up to the starting melting temperature. The present study also highlights that, in contrast with the case of Au₂₀, a single electron excess or deficiency is not determinant regarding the melting characteristics, even quantitatively, for clusters containing 55 atoms, for gold as for silver.

Nested Basin-Sampling

We report an embarrassingly parallel method for the evaluation of thermodynamic properties over an energy landscape exhibiting broken ergodicity, nested is the likelihood of the observed data D givenbasin-sampling (NBS). We also introduce the No Galilean U-Turn Sampler (NoGUTS), a new sampling scheme based on the No U-Turn Sampler (NUTS) introduced by Hoffman and Gelman (2014) that works with the Galilean Monte Carlo scheme introduced by Betancourt (2012) to aid the efficient generation of new live points. NoGUTS can be thought of as a form of reflective slice sampling with an automatic stopping criterion. We apply this approach to a benchmark atomic cluster of 31 Lennard-Jones atoms, which exhibits a low temperature solid-solid heat capacity peak. The calculated heat capacity is compared with results generated by parallel tempering (PT), basin-sampling parallel tempering (BSPT), and standard nested sampling (NS) simulations. NBS reproduces the full heat capacity curve predicted by PT and BSPT, while the NS calculation with similar computational cost fails to resolve the low-temperature solid-solid phase transition.

Diagrams of States of Single Flexible-Semiflexible Multi-Block Copolymer Chains: A Flat-Histogram Monte Carlo Study

The combination of flexibility and semiflexibility in a single molecule is a powerful design principle both in nature and in materials science. We present results on the conformational behavior of a single multiblock-copolymer chain, consisting of equal amounts of Flexible (F) and Semiflexible (S) blocks with different affinity to an implicit solvent. We consider a manifold of macrostates defined by two terms in the total energy: intermonomer interaction energy and stiffness energy. To obtain diagrams of states (pseudo-phase diagrams), we performed flat-histogram Monte Carlo simulations using the Stochastic Approximation Monte Carlo algorithm (SAMC). We have accumulated two-Dimensional Density of States (2D DoS) functions (defined on the 2D manifold of macrostates) for a SF-multiblock-copolymer chain of length N=64 with block lengths b = 4, 8, 16, and 32 in two different selective solvents. In an analysis of the canonical ensemble, we calculated the heat capacity and determined its maxima and the most probable morphologies in different regions of the state diagrams. These are rich in various, non-trivial morphologies, which are formed without any specific interactions, and depend on the block length and the type of solvent selectivity (preferring S or F blocks, respectively). We compared the diagrams with those for the non-selective solvent and reveal essential changes in some cases. Additionally, we implemented microcanonical analysis in the “conformational” microcanonical (NVU, where U is the potential energy) and the true microcanonical (NVE, where E is the total energy) ensembles with the aim to reveal and classify pseudo-phase transitions, occurring under the change of temperature.

Protocol Dependence and State Variables in the Force-Moment Ensemble

Stress-based ensembles incorporating temperaturelike variables have been proposed as a route to an equation of state for granular materials. To test the efficacy of this approach, we perform experiments on a two-dimensional photoelastic granular system under three loading conditions: uniaxial compression, biaxial compression, and simple shear. From the interparticle forces, we find that the distributions of the normal component of the coarse-grained force-moment tensor are exponential tailed, while the deviatoric component is Gaussian distributed. This implies that the correct stress-based statistical mechanics conserves both the force-moment tensor and the Maxwell-Cremona force-tiling area. As such, two variables of state arise: the tensorial angoricity (α[over ^]) and a new temperaturelike quantity associated with the force-tile area which we name keramicity (κ). Each quantity is observed to be inversely proportional to the global confining pressure; however, only κ exhibits the protocol independence expected of a state variable, while α[over ^] behaves as a variable of process.

Density-functional tight-binding approach for metal clusters, nanoparticles, surfaces and bulk: application to silver and gold

Density-functional based tight-binding (DFTB) is an efficient quantum mechanical method that can describe a variety of systems, going from organic and inorganic compounds to metallic and hybrid materials. The present topical review addresses the ability and performance of DFTB to investigate energetic, structural, spectroscopic and dynamical properties of gold and silver materials. After a brief overview of the theoretical basis of DFTB, its parametrization and its transferability, we report its past and recent applications to gold and silver systems, including small clusters, nanoparticles, bulk and surfaces, bare and interacting with various organic and inorganic compounds. The range of applications covered by those studies goes from plasmonics and molecular electronics, to energy conversion and surface chemistry. Finally, perspectives of DFTB in the field of gold and silver surfaces and NPs are outlined.

Exploring Energy Landscapes

Recent advances in the potential energy landscapes approach are highlighted, including both theoretical and computational contributions. Treating the high dimensionality of molecular and condensed matter systems of contemporary interest is important for understanding how emergent properties are encoded in the landscape and for calculating these properties while faithfully representing barriers between different morphologies. The pathways characterized in full dimensionality, which are used to construct kinetic transition networks, may prove useful in guiding such calculations. The energy landscape perspective has also produced new procedures for structure prediction and analysis of thermodynamic properties. Basin-hopping global optimization, with alternative acceptance criteria and generalizations to multiple metric spaces, has been used to treat systems ranging from biomolecules to nanoalloy clusters and condensed matter. This review also illustrates how all this methodology, developed in the context of chemical physics, can be transferred to landscapes defined by cost functions associated with machine learning.

Neural network potentials for dynamics and thermodynamics of gold nanoparticles

For understanding the dynamical and thermodynamical properties of metal nanoparticles, one has to go beyond static and structural predictions of a nanoparticle. Accurate description of dynamical properties may be computationally intensive depending on the size of nanoparticle. Herein, we demonstrate the use of atomistic neural network potentials, obtained by fitting quantum mechanical data, for extensive molecular dynamics simulations of gold nanoparticles. The fitted potential was tested by performing global optimizations of size selected gold nanoparticles (Au_n, 17 ≤ n ≤ 58). We performed molecular dynamics simulations in canonical (NVT) and microcanonical (NVE) ensembles on Au₁₇, Au₃₄, Au₅₈ for a total simulation time of around 3 ns for each nanoparticle. Our study based on both NVT and NVE ensembles indicate that there is a dynamical coexistence of solid-like and liquid-like phases near melting transition. We estimate the probability at finite temperatures for set of isomers lying below 0.5 eV from the global minimum structure. In the case of Au₁₇ and Au₅₈, the properties can be estimated using global minimum structure at room temperature, while for Au₃₄, global minimum structure is not a dominant structure even at low temperatures.

Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition.

Networks of interacting units are ubiquitous in various fields of biology; e.g. gene regulatory networks, neuronal networks, social structures. If a limited set of observables is accessible, maximum-entropy models provide a way to construct a statistical model for such networks, under particular assumptions. The pairwise maximum-entropy model only uses the first two moments among those observables, and can be interpreted as a network with only pairwise interactions. If correlations are on average positive, we here show that the maximum entropy distribution tends to become bimodal. In the application to neuronal activity this is a problem, because the bimodality is an artefact of the statistical model and not observed in real data. This problem could also affect other fields in biology. We here explain under which conditions bimodality arises and present a solution to the problem by introducing a collective negative feedback, corresponding to a modified maximum-entropy model. This result may point to the existence of a homeostatic mechanism active in the system that is not part of our set of observable units.

Thermal rectification in mass-graded next-nearest-neighbor Fermi-Pasta-Ulam lattices

We study the thermal rectification efficiency, i.e., quantification of asymmetric heat flow, of a one-dimensional mass-graded anharmonic oscillator Fermi-Pasta-Ulam lattice both with nearest-neighbor (NN) and next-nearest-neighbor (NNN) interactions. The system presents a maximum rectification efficiency for a very precise value of the parameter that controls the coupling strength of the NNN interactions, which also optimizes the rectification figure when its dependence on mass asymmetry and temperature differences is considered. The origin of the enhanced rectification is the asymmetric local heat flow response as the heat reservoirs are swapped when a finely tuned NNN contribution is taken into account. A simple theoretical analysis gives an estimate of the optimal NNN coupling in excellent agreement with our simulation results.

Canonical free-energy barrier of particle and polymer cluster formation

A common approach to study nucleation rates is the estimation of free-energy barriers. This usually requires knowledge about the shape of the forming droplet, a task that becomes notoriously difficult in macromolecular setups starting with a proper definition of the cluster boundary. Here we demonstrate a shape-free determination of the free energy for temperature-driven cluster formation in particle as well as polymer systems. Combined with rigorous results on equilibrium droplet formation, this allows for a well-defined finite-size scaling analysis of the effective interfacial free energy at a fixed density. We first verify the theoretical predictions for the formation of a liquid droplet in a supersaturated particle gas by generalized-ensemble Monte Carlo simulations of a Lennard-Jones system. Going one step further, we then generalize this approach to cluster formation in a dilute polymer solution. Our results suggest an analogy with particle condensation, when the macromolecules are interpreted as extended particles.

Composition- and temperature-dependent liquid structures in Al-Cu alloys: an ab initio molecular dynamics and x-ray diffraction study

The composition- and temperature-dependent liquid structures in eight Al_rich-Cu binary alloys (from hypoeutectic Al₉₃Cu₇ to hypereutectic Al₇₀Cu₃₀) have been experimentally and computationally studied by x-ray diffraction (XRD) experiments and ab initio molecular dynamics (AIMD) simulations. The remarkable agreements of structure factors for all liquid Al_rich-Cu alloys obtained from high-temperature high-energy XRD measurements and AIMD simulations have been achieved, which consolidates the analyses of structural evolutions in Al_rich-Cu liquids during the cooling processing by AIMD simulations. The heat capacity of liquid Al_rich-Cu alloys continuously increases and presents no abnormal peak when reducing the temperature, which differs from the reported prediction for 55-atom Al_rich-Cu nanoliquids. The diffusivities of Al and Cu undergo an increasing deviation from Arrhenius behavior by tuning Cu concentration from 7 to 30 atomic percentages, correlated to the local ordering in these liquids by means of coordination number, bond-angle distribution, Honeycutt-Andersen index, bond-orientational order and Voronoi tessellation analyses. Upon cooling, the microstructure of the liquid Al_rich-Cu alloys inclines to form Al₂Cu crystal-like local atomic ordering, especially in the hypereutectic liquids. The favorable short-range ordering between Cu and Al atoms could cause the non-Arrhenius diffusion behavior.

Theoretical investigation of the solid–liquid phase transition in protonated water clusters

Molecular dynamics simulations provide an atomistic scale description of the phase transition in protonated water clusters (H₂O)_nH⁺(n= 20–23) and an interpretation to recent nano-calorimetric experiments.

Dynamics and thermodynamics of the coronene octamer described by coarse-grained potentials

Coarse-grained models developed for polycyclic aromatic hydrocarbons based on the Paramonov–Yaliraki potential have been employed to investigate the finite temperature thermodynamics, out-of-equilibrium dynamics, energy landscapes, and rearrangement pathways of the coronene octamer.

Anharmonic densities of states: A general dynamics-based solution

Density of states is a fundamental physical characteristic that lies at the foundation of statistical mechanics and theoretical constructs that derive from them (e.g., kinetic rate theories, phase diagrams, and others). Even though most real physical systems are anharmonic, the vibrational density of states is customarily treated within the harmonic approximation, or with some partial, often limited, account for anharmonicity. The reason for this is that the problem of anharmonic densities of states stubbornly resisted a general and exact, yet convenient and straightforward in applications, solution. Here we formulate such a solution within both classical and quantum mechanics. It is based on actual dynamical behavior of systems as a function of energy and as observed, or monitored, on a chosen time scale, short or long. As a consequence, the resulting anharmonic densities of states are fully dynamically informed and, in general, time-dependent. As such, they lay the ground for formulation of new statistical mechanical frameworks that incorporate time and are ergodic, by construction, with respect to actual dynamical behavior of systems.

Self-diffusion and structural properties of confined fluids in dynamic coexistence

Self-diffusion and radial distribution functions are studied in a strongly confined Lennard-Jones fluid. Surprisingly, in the solid-liquid phase transition region, where the system exhibits dynamic coexistence, the self-diffusion constants are shown to present up to three-fold variations from solid to liquid phases at fixed temperature, while the radial distribution function corresponding to both the liquid and the solid phases are essentially indistinguishable.

A Two-Dimensional Liquid Structure Explains the Elevated Melting Temperatures of Gallium Nanoclusters

Melting in finite-sized materials differs in two ways from the solid-liquid phase transition in bulk systems. First, there is an inherent scaling of the melting temperature below that of the bulk, known as melting point depression. Second, at small sizes changes in melting temperature become nonmonotonic and show a size-dependence that is sensitive to the structure of the particle. Melting temperatures that exceed those of the bulk material have been shown to occur for a very limited range of nanoclusters, including gallium, but have still never been ascribed a convincing physical explanation. Here, we analyze the structure of the liquid phase in gallium clusters based on molecular dynamics simulations that reproduce the greater-than-bulk melting behavior observed in experiments. We observe persistent nonspherical shape distortion indicating a stabilization of the surface, which invalidates the paradigm of melting point depression. This shape distortion suggests that the surface acts as a constraint on the liquid state that lowers its entropy relative to that of the bulk liquid and thus raises the melting temperature.

Phase changes of the water hexamer and octamer in the gas phase and adsorbed on polycyclic aromatic hydrocarbons

We investigate thermodynamic properties of small water clusters adsorbed on polycyclic aromatic hydrocarbons (PAHs), which are relevant systems in the context of astrophysical and atmospheric chemistry. We present heat capacity curves computed from parallel-tempering molecular dynamics and Monte Carlo simulations that were performed using the self-consistent-charge density-functional based tight-binding method. These curves are characteristic of the phase changes occurring in the aggregates and provide useful information on the evolution of the interaction between the water molecules and the PAHs as a function of temperature. After benchmarking our approach on the water hexamer and octamer in the gas phase, we present some results for these same clusters adsorbed on coronene and circumcoronene. When compared to the curves obtained for the isolated water clusters, the phase change temperature significantly decreases for the (H2O)8-PAH clusters whereas it depends on the nature of the PAH in the case of the hexamer. We analyse these differences as connected to the relative energies of the optimized characteristic isomers and to their dynamical behavior. We also evidence the population changes of the various cluster isomers as a function of temperature.

Equilibrium molecular thermodynamics from Kirkwood sampling

We present two methods for barrierless equilibrium sampling of molecular systems based on the recently proposed Kirkwood method (J. Chem. Phys.2009, 130, 134102). Kirkwood sampling employs low-order correlations among internal coordinates of a molecule for random (or non-Markovian) sampling of the high dimensional conformational space. This is a geometrical sampling method independent of the potential energy surface. The first method is a variant of biased Monte Carlo, where Kirkwood sampling is used for generating trial Monte Carlo moves. Using this method, equilibrium distributions corresponding to different temperatures and potential energy functions can be generated from a given set of low-order correlations. Since Kirkwood samples are generated independently, this method is ideally suited for massively parallel distributed computing. The second approach is a variant of reservoir replica exchange, where Kirkwood sampling is used to construct a reservoir of conformations, which exchanges conformations with the replicas performing equilibrium sampling corresponding to different thermodynamic states. Coupling with the Kirkwood reservoir enhances sampling by facilitating global jumps in the conformational space. The efficiency of both methods depends on the overlap of the Kirkwood distribution with the target equilibrium distribution. We present proof-of-concept results for a model nine-atom linear molecule and alanine dipeptide.

Roles of dynamical symmetry breaking in driving oblate-prolate transitions of atomic clusters

This paper explores the driving mechanisms for structural transitions of atomic clusters between oblate and prolate isomers. We employ the hyperspherical coordinates to investigate structural dynamics of a seven-atom cluster at a coarse-grained level in terms of the dynamics of three gyration radii and three principal axes, which characterize overall mass distributions of the cluster. Dynamics of gyration radii is governed by two kinds of forces. One is the potential force originating from the interactions between atoms. The other is the dynamical forces called the internal centrifugal forces, which originate from twisting and shearing motions of the system. The internal centrifugal force arising from twisting motions has an effect of breaking the symmetry between two gyration radii. As a result, in an oblate isomer, activation of the internal centrifugal force that has the effect of breaking the symmetry between the two largest gyration radii is crucial in triggering structural transitions into prolate isomers. In a prolate isomer, on the other hand, activation of the internal centrifugal force that has the effect of breaking the symmetry between the two smallest gyration radii is crucial in triggering structural transitions into oblate isomers. Activation of a twisting motion that switches the movement patterns of three principal axes is also important for the onset of structural transitions between oblate and prolate isomers. Based on these trigger mechanisms, we finally show that selective activations of specific gyration radii and twisting motions, depending on the isomer of the cluster, can effectively induce structural transitions of the cluster. The results presented here could provide further insights into the control of molecular reactions.

Thermodynamics of nanoalloys

This article reviews recent advances in our understanding of how temperature affects the structure and the phase of multimetallic nanoparticles. Focusing on bimetallic systems, we discuss the interplay of size, shape and chemical order on the stable configurations at thermal equilibrium. Besides some considerations about experimental evidence for thermally-induced transformations, most insight is generally gained from theory and computation. The perspectives offered by mesoscopic approaches (i.e. corrected from the bulk) and atomistic simulations complement each other and often provide detailed information about the respective roles of coordination, composition and more generally surface effects to be evaluated. Order-disorder transitions and the melting phase change are strongly altered in nanoscale systems, and we describe how they possibly impact entire phase diagrams.

Evolution of atomic structure in Al75Cu25 liquid from experimental and ab initio molecular dynamics simulation studies

X-ray diffraction and electrostatic levitation measurements, together with the ab initio molecular dynamics simulation of liquid Al(75)Cu(25) alloy have been performed from 800 to 1600 K. Experimental and ab initio molecular dynamics simulation results match well with each other. No abnormal changes were experimentally detected in the specific heat capacity over total hemispheric emissivity and density curves in the studied temperature range for a bulk liquid Al(75)Cu(25) alloy measured by the electrostatic levitation technique. The structure factors gained by the ab initio molecular dynamics simulation precisely coincide with the experimental data. The atomic structure analyzed by the Honeycutt-Andersen index and Voronoi tessellation methods shows that icosahedral-like atomic clusters prevail in the liquid Al(75)Cu(25) alloy and the atomic clusters evolve continuously. All results obtained here suggest that no liquid-liquid transition appears in the bulk liquid Al(75)Cu(25) alloy in the studied temperature range.

Phase diagrams of nanoalloys: influence of size and morphology

A non-monotonous variation of the critical temperature as a function of the length of the nanotubes.

Al20+ does melt, albeit above the bulk melting temperature of aluminium

Employing first principles parallel tempering molecular dynamics in the microcanonical ensemble, we report the presence of a clear solid–liquid-like melting transition in Al₂₀⁺ clusters, not found in experiments.

Where macro meets micro

Reconciling or somehow linking the macroscopic and microscopic approaches to chemical and physical processes has been a challenge unaddressed for many years. One approach, presented here, treats the issue by examining individual phenomena well described by a macro approach that fails when applied to small systems. The key to the approach is determining the approximate system size below which the breakdown of the macro description is observable. The most developed example is the failure of the Gibbs phase rule for sufficiently small atomic clusters. Other examples, such as the onset, at sufficient size, of the insulator-to-metal transition, are discussed, as are some still more challenging phenomena.

Thermostatistical description of small systems in nonequilibrium conditions: energy conversion and harvesting

Hysteresis cycles are very important features of energy conversion and harvesting devices, such as batteries. The efficiency of these may be strongly affected by the physical size of the system. Here, we show that in systems which are small enough, the existence of physical boundaries which produce nonhomogeneities of the interaction potential gives rise to inflections and barriers in the associated free energy. This in turn brings on irreversible processes which can be triggered under suitable external conditions imposed by a heat bath. As an example, by controlling the temperature, the state of a small system may be impelled to oscillate between two different structural configurations or aggregation states avoiding equilibrium coexistence and therefore dissipating energy. This cyclical behavior associated with a hysteresis cycle may be prototypical of energy conversion, storage, or generating nanodevices, as exemplified by Li-ion insertion batteries.

Nanothermodynamics of metal nanoparticles

This article presents a perspective on thermodynamic characterization of metal nanoparticles by computational chemistry. Topics emphasized include structural stability, phases, phase changes, and free energy functions of aluminum nanoparticles.