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 CoN, PtN, and FeN (N = 3-200) clusters. The performance of the SaNSDE algorithm was first evaluated by comparing our results with the parallel results collected in the Cambridge Cluster Database (CCD). Subsequently, different analytical methods were introduced to investigate the structural and energetic properties of these clusters systematically, and special attention was paid to elucidating the structural evolution with cluster size by exploring their overall shape, atomic arrangement, structural similarity, and growth pattern. By comparison with those results listed in the CCD, 13 lower-energy structures of FeN clusters were discovered. Moreover, our results reveal that the clusters of three metals had different magic numbers with superior stable structures, most of which possessed high symmetry. The structural evolution of Co, Pt, and Fe clusters could be, respectively, considered as predominantly closed-shell icosahedral, Marks decahedral, and disordered icosahedral-ring growth. Further, the formation of shell structures was discovered, and the clusters with hcp-, fcc-, and bcc-like configurations were ascertained. Nevertheless, the growth of the clusters was not simply atom-to-atom piling up on a given cluster despite gradual saturation of the coordination number toward its bulk limit. Our work identifies the general growth trends for such a wide region of cluster sizes, which would be unbearably expensive in first-principles calculations, and advances the development of global optimization algorithms for the structural prediction of clusters.

Effect of composition and architecture on the thermodynamic behavior of AuCu nanoparticles.

The chemical and physical properties of nanomaterials ultimately rely on their crystal structures, chemical compositions and distributions. In this paper, a series of AuCu bimetallic nanoparticles with well-defined architectures and variable compositions has been addressed to explore their thermal stability and thermally driven behavior by molecular dynamics simulations. By combination of energy and Lindemann criteria, the solid-liquid transition and its critical temperature were accurately identified. Meanwhile, atomic diffusion, bond order, and particle morphology were examined to shed light on thermodynamic evolution of the particles. Our results reveal that composition-dependent melting point of AuCu nanoparticles significantly departs from the Vegard's law prediction. Especially, chemically disordered (ordered) alloy nanoparticles exhibited markedly low (high) melting points in comparison with their unary counterparts, which should be attributed to enhancing (decreasing) atomic diffusivity in alloys. Furthermore, core-shell structures and heterostructures demonstrated a mode transition between the ordinary melting and the two-stage melting with varying Au content. AuCu alloyed nanoparticles presented the evolution tendency of chemical ordering from disorder to order before melting and then to disorder during melting. Additionally, as the temperature increases, the shape transformation was observed in AuCu nanoparticles with heterostructure or L10 structure owing to the difference in thermal expansion coefficients of elements and/or of crystalline orientations. Our findings advance the fundamental understanding on thermodynamic behavior and stability of metallic nanoparticles, offering theoretical insights for design and application of nanosized particles with tunable properties.

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.

Processing Magnoliae Officinalis Cortex with ginger juice: process optimization based on AHP-CRITIC weighting method and composition changes after processing / 中国中药杂志

The weight coefficients of appearance traits, extract yield of standard decoction, and total content of honokiol and magnolol were determined by analytic hierarchy process(AHP), criteria importance though intercrieria correlation(CRITIC), and AHP-CRITIC weighting method, and the comprehensive scores were calculated. The effects of ginger juice dosage, moistening time, proces-sing temperature, and processing time on the quality of Magnoliae Officinalis Cortex(MOC) were investigated, and Box-Behnken design was employed to optimize the process parameters. To reveal the processing mechanism, MOC, ginger juice-processed Magnoliae Officinalis Cortex(GMOC), and water-processed Magnoliae Officinalis Cortex(WMOC) were compared. The results showed that the weight coefficients of the appearance traits, extract yield of standard decoction, and total content of honokiol and magnolol determined by AHP-CRITIC weighting method were 0.134, 0.287, and 0.579, respectively. The optimal processing parameters of GMOC were ginger juice dosage of 8%, moistening time of 120 min, and processing at 100 ℃ for 7 min. The content of syringoside and magnolflorine in MOC decreased after processing, and the content of honokiol and magnolol followed the trend of GMOC>MOC>WMOC, which suggested that the change in clinical efficacy of MOC after processing was associated with the changes of chemical composition. The optimized processing technology is stable and feasible and provides references for the modern production and processing of MOC.