Unbiased structural search of small copper clusters within DFT

Hierarchical structures and magnetism of Co clusters: a perspective from integration of deep learning and a hybrid differential evolution algorithm

Theoretically determining the lowest-energy structure of a cluster has been a persistent challenge due to the inherent difficulty in accurate description of its potential energy surface (PES) and the exponentially increasing number of local minima on the PES with the cluster size. In this work, density-functional theory (DFT) calculations of Co clusters were performed to construct a dataset for training deep neural networks to deduce a deep potential (DP) model with near-DFT accuracy while significantly reducing computational consumption comparable to classic empirical potentials. Leveraging the DP model, a high-efficiency hybrid differential evolution (HDE) algorithm was employed to search for the lowest-energy structures of CoN (N = 11-50) clusters. Our results revealed 38 of these clusters superior to those recorded in the Cambridge Cluster Database and identified diverse architectures of the clusters, evolving from layered structures for N = 11-27 to Marks decahedron-like structures for N = 28-42 and to icosahedron-like structures for N = 43-50. Subsequent analyses of the atomic arrangement, structural similarity, and growth pattern further verified their hierarchical structures. Meanwhile, several highly stable clusters, i.e., Co13, Co19, Co22, Co39, and Co43, were discovered by the energetic analyses. Furthermore, the magnetic stability of the clusters was verified, and a competition between the coordination number and bond length in affecting the magnetic moment was observed. Our study provides high-accuracy and high-efficiency prediction of the optimal structures of clusters and sheds light on the growth trend of Co clusters containing tens of atoms, contributing to advancing the global optimization algorithms for effective determination of cluster structures.

Theoretical Insights into Synergistic Effects at Cu/TiC Interfaces for Promoting CO2 Activation

The adsorption behaviors of CO2 at the Cu n /TiC(001) interfaces (n = 1-8) have been investigated using the density functional theory method. Our results reveal that the introduction of copper clusters on a TiC surface can significantly improve the thermodynamic stability of CO2 chemisorption. However, the most stable adsorption site is sensitive to the size and morphology of Cu n particles. The interfacial configuration is the most stable structure for copper clusters with small (n ≤ 2) and large (n ≥ 8) sizes, in which both Cu particles and TiC support are involved in CO2 activation. In such a case, the synergistic behavior is associated with the ligand effect introduced by directly forming adsorption bonds with CO2. For those Cu n clusters with a medium size (n = 3-7), the configuration where CO2 adsorbs solely on the exposed hollow site constructed by Cu atoms at the interface shows the best stability, and the charger transfer becomes the primary origin of the synergistic effect in promoting CO2 activation. Since the most obvious deformation of CO2 is observed for the TiC(001)-surface-supported Cu4 and Cu7 particles, copper clusters with specific sizes of n = 4 and 7 exhibit the best ability for CO2 activation. Furthermore, the kinetic barriers for CO2 dissociation on Cu4- and Cu7-supported TiC surfaces are determined. The findings obtained in this work provide useful insights into optimizing the Cu/TiC interface with high catalytic activation of CO2 by precisely controlling the size and dispersion of copper particles.

New insight into the mechanism of carbon dioxide activation on copper-based catalysts: A theoretical study

A combination of Artificial Bee Colony algorithm, eXtended Tight Binding and Density functional theory methods were performed to study the activation process of carbon dioxide (CO₂) over copper (Cu₄ cluster) based catalytic systems. The findings revealed that the activation of the C-O bond resulted from the electron transfer to σ*, π* - MO of CO₂. The more the electrons are transferred to CO₂, the more the C-O bond is activated and elongated. The suitability of several metal oxide supports (Fe₂O₃, Al₂O₃, MgO, ZnO) is estimated using calculated electronic parameters (global electrophilicity index, vertical ionization potential and vertical electron affinity). Aside from demonstrating the appropriateness of Al₂O₃ and ZnO, a thorough examination of MgO revealed that, due to the formation of stable carbonate products, this oxide is not really appropriate as a support for copper-based catalysts in CO₂ conversion. Our studies have also shown that the electron enrichment of copper atoms plays a key role in the activation of C-O bonds. Alkali metal doping (Li, K, Cs) significantly improves the catalytic efficiency of the Cu₄ cluster. Based on the results of electron transfer to the CO₂ molecule, the effect of doping alkali metal atoms may be organized in the following order: Cs > K > Li. A new core/shell catalytic system with potassium atoms in the core and copper atoms in the shell has been proposed and has proven to be a promising, efficient catalytic system in the CO₂ adsorption and activation.

Activation of CO2 by supported Cu clusters

CO₂ forms a bent, negative anion upon adsorption near a Cu₃ cluster supported on TiO₂.