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 Co ( = 11-50) clusters.

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 Co, Pt, and Fe ( = 3-200) clusters.

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.

Modelling free and oxide-supported nanoalloy catalysts: comparison of bulk-immiscible Pd-Ir and Au-Rh systems and influence of a TiO support.

The relative stabilities of different chemical arrangements of Pd-Ir and Au-Rh nanoalloys (and their pure metal equivalents) are studied, for a range of compositions, for fcc truncated octahedral 38- and 79-atom nanoparticles (NPs). For the 38-atom NPs, comparisons are made of pure and alloy NPs supported on a TiO2(110) slab. The relative energies of different chemical arrangements are found to be similar for Pd-Ir and Au-Rh nanoalloys, and depend on the cohesive and surface energies of the component metals.

DFT study of the structure, chemical ordering and molecular adsorption of Pd-Ir nanoalloys.

The structures and surface adsorption sites of Pd-Ir nanoalloys are crucial to the understanding of their catalytic performance because they can affect the activity and selectivity of nanocatalysts. In this article, density functional theory (DFT) calculations are performed on bare Pd-Ir nanoalloys to systematically explore their stability and chemical ordering properties, before studying the adsorption of CO on the nanoalloys. First, the structural stability of 38-atom and 79-atom truncated octahedral (TO) Pd-Ir nanoalloys are investigated.