Structure determination in 55-atom Li–Na and Na–K nanoalloys

A method for predicting basins in the global optimization of nanoclusters with applications to AlxCuy alloys

The problem of obtaining the geometrical configuration of a molecule that minimizes its potential energy is a very complicated one for a series of applications, ranging from determining the structure of biological macromolecules to nanoclusters of atoms. Global optimization tools are available for this task, and many of them are based in performing successive local optimizations, where the starting geometries for these steps are determined by an intelligent algorithm. Here we develop a method to save computing time in the optimization of nanoclusters by predicting if a given minimum has been previously visited during local optimization steps. Our application to Cu-Al nanoalloys indicates that it is possible to save a substantial amount of computational cost. The application also reveals new promising Al_xCu_y clusters and explain their stabilities in terms of the jellium model.

An ab initio investigation of the adsorption properties of water on binary AlSi clusters

Nanoalloys represent potential catalysts for the water splitting reaction. The water–cluster interaction is a key aspect of the process, but is not fully understood. This work provides an in-depth study and insights into the AlxSiy·H₂O case.

Simulating the NaK Eutectic Alloy with Monte Carlo and Machine Learning

Combining atomistic simulations and machine learning techniques can expedite significantly the materials discovery process. We present an application of such methodological combination for the prediction of the melting transition and amorphous-solid behavior of the NaK alloy at the eutectic concentration. We show that efficient prediction of these properties is possible via machine learning methods trained on the topological local structural properties. The configurations resulting from Monte Carlo annealing of the NaK eutectic alloy are analyzed with topological attributes based on the Voronoi tessellation and using expectation-maximization clustering and Random Forest classification. We show that the Voronoi topological fingerprints make an accurate and fast prediction of the alloy thermal behavior by cataloguing the atomic configurations into three distinct phases: liquid, amorphous solid, and crystalline solid. Melting is found at 230 K by the sharp split of configurations classified as crystalline solid and as liquid. With the proposed metrics, an arrest-motion temperature is identified at 130–140 K through a top down clustering of the atomic configurations catalogued as amorphous solid. This statistical learning paradigm is not restricted to eutectic alloys or thermodynamics, extends the utility of topological attributes in a significant way, and harnesses the discovery of new material properties.

Structural and homotop optimization of neutral Al–Si nanoclusters

The geometry and stability of aluminum–silicon alloys up to 13 atoms are investigated using electronic structure methods.

Structure and stability of neutral Al–Mg nanoclusters up to 55 atoms

The geometries of aluminum–magnesium nanoalloys are explored using a genetic algorithm tuned to search for the 10 lowest energy minima for each cluster size and composition.

Electronic binding energy and thermal relaxation of Li and LiNa atomic alloying clusters

We examined the effects of atomic hetero- and under-coordination on the relaxation of the interatomic bonding and electronic binding energy of Li and LiNa cluster alloying using a combination of the bond-order-length-strength correlation and density functional theory calculations.

Vegard's law-like behavior for Mn(m)Tc(n) alloy clusters: a first-principles prediction

With a view to gaining an understanding of the alloying tendency of bimetallic nanoalloy clusters of isoelectronic constituents, we studied the structural and mixing behavior of MnmTcn alloy clusters with m + n = 13 for all possible compositions, using first-principles electronic structure calculations. Our study reports a favorable mixing tendency for the alloy clusters. The average bond lengths of the minimum energy structures show an overall linear variation with concentration, indicating a Vegard's law-like variation for the nanoalloy clusters, though the optimized structures undergo a structural transition from a closed and compact structure for the Mn-rich alloy clusters to an open layered-like structure for the Tc-rich alloy clusters. We work out a continuous and smooth interplay between hybridization and magnetization properties of the alloy clusters, which plays a vital role in the Vegard's law-like variation in their average bond lengths.

Theoretical study of small sodium–potassium alloy clusters through genetic algorithm and quantum chemical calculations

Structures regarding the growth of sodium–potassium clusters obtained employing electronic structure methods—from high level coupled cluster calculations to all-electrons correlated MP2 and density functional theory.