What Electronic Structure Method Can Be Used in the Global Optimization of Nanoclusters?

Underlying mechanisms of gold nanoalloys stabilization

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core-shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au's surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys' preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.

A database of low-energy atomically precise nanoclusters

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies.

A Practical Algorithm to Solve the Near-Congruence Problem for Rigid Molecules and Clusters

We present an improved algorithm to solve the near-congruence problem for rigid molecules and clusters based on the iterative application of assignment and alignment steps with biased Euclidean costs. The algorithm is formulated as a quasi-local optimization procedure with each optimization step involving a linear assignment (LAP) and a singular value decomposition (SVD). The efficiency of the algorithm is increased by up to 5 orders of magnitude with respect to the original unbiased noniterative method and can be applied to systems with hundreds or thousands of atoms, outperforming all state-of-the-art methods published so far in the literature. The Fortran implementation of the algorithm is available as an open source library (https://github.com/qcuaeh/molalignlib) and is suitable to be used in global optimization methods for the identification of local minima or basins.

Role of atomicity in the oxygen reduction reaction activity of platinum sub nanometer clusters: A global optimization study

Metal nanoclusters are an important class of materials for catalytic applications. Sub nanometer clusters are relatively less explored for their catalytic activity on account of undercoordinated surface structure. Taking this into account, we studied platinum-based sub nanometer clusters for their catalytic activity for oxygen reduction reaction (ORR). A comprehensive analysis with global optimization is carried out for structural prediction of the platinum clusters. The energetic and electronic properties of interactions of clusters with reaction intermediates are investigated. The role of structural sensitivity in the dynamics of clusters is unraveled, and unique intermediate specific interactions are identified. ORR energetics is examined, and exceptional activity for sub nanometer clusters are observed. An inverse size versus activity relationship is identified, challenging the conventional trends followed by larger nanoclusters. The principal role of atomicity in governing the catalytic activity of nanoclusters is illustrated. The structural norms governing the sub nanometer cluster activity are shown to be markedly different from larger nanoclusters.

Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques

We propose a new methodology to study, at the density functional theory (DFT) level, the clusters resulting from the microsolvation of alkali-metal ions with rare-gas atoms. The workflow begins with a global optimization search to generate a pool of low-energy minimum structures for different cluster sizes. This is achieved by employing an analytical potential energy surface (PES) and an evolutionary algorithm (EA). The next main stage of the methodology is devoted to establish an adequate DFT approach to treat the microsolvation system, through a systematic benchmark study involving several combinations of functionals and basis sets, in order to characterize the global minimum structures of the smaller clusters. In the next stage, we apply machine learning (ML) classification algorithms to predict how the low-energy minima of the analytical PES map to the DFT ones. An early and accurate detection of likely DFT local minima is extremely important to guide the choice of the most promising low-energy minima of large clusters to be re-optimized at the DFT level of theory. In this work, the methodology was applied to the Li+Krn (n = 2-14 and 16) microsolvation clusters for which the most competitive DFT approach was found to be the B3LYP-D3/aug-pcseg-1. Additionally, the ML classifier was able to accurately predict most of the solutions to be re-optimized at the DFT level of theory, thereby greatly enhancing the efficiency of the process and allowing its applicability to larger clusters.

Structural Stability of the CO2 @sI Hydrate: a Bottom-Up Quantum Chemistry Approach on the Guest-Cage and Inter-Cage Interactions

Through reliable first-principles computations, we have demonstrated the impact of CO₂ molecules enclathration on the stability of sI clathrate hydrates. Given the delicate balance between the interaction energy components (van der Waals, hydrogen bonds) present on such systems, we follow a systematic bottom-up approach starting from the individual 5¹² and 5¹² 6² sI cages, up to all existing combinations of two-adjacent sI crystal cages to evaluate how such clathrate-like models perform on the evaluation of the guest-host and first-neighbors inter-cage effects, respectively. Interaction and binding energies of the CO₂ occupation of the sI cages were computed using DF-MP2 and different DFT/DFT-D electronic structure methodologies. The performance of selected DFT functionals, together with various semi-classical dispersion corrections schemes, were validated by comparison with reference ab initio DF-MP2 data, as well as experimental data from x-ray and neutron diffraction studies available. Our investigation confirms that the inclusion of the CO₂ in the cage/s is an energetically favorable process, with the CO₂ molecule preferring to occupy the large 5¹² 6² sI cages compared to the 5¹² ones. Further, the present results conclude on the rigidity of the water cages arrangements, showing the importance of the inter-cage couplings in the cluster models under study. In particular, the guest-cage interaction is the key factor for the preferential orientation of the captured CO₂ molecules in the sI cages, while the inter-cage interactions seems to cause minor distortions with the CO₂ guest neighbors interactions do not extending beyond the large 5¹² 6² sI cages. Such findings on these clathrate-like model systems are in accord with experimental observations, drawing a direct relevance to the structural stability of CO₂ @sI clathrates.

Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters

In this work, we explore the possibility of using computationally inexpensive electronic structure methods, such as semiempirical and DFTB calculations, for the search of the global minimum (GM) structure of chemical systems. The basic prerequisite that these inexpensive methods will need to fulfill is that their lowest energy structures can be used as starting point for a subsequent local optimization at a benchmark level that will yield its GM. If this is possible, one could bypass the global optimization at the expensive method, which is currently impossible except for very small molecules. Specifically, we test our methods with clusters of second row elements including systems of several bonding types, such as alkali, metal, and covalent clusters. The results reveal that the DFTB3 method yields reasonable results and is a potential candidate for this type of applications. Even though the DFTB2 approach using standard parameters is proven to yield poor results, we show that a re-parametrization of only its repulsive part is enough to achieve excellent results, even when applied to larger systems outside the training set.

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.