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.

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.

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.

[Processing Magnoliae Officinalis Cortex with ginger juice: process optimization based on AHP-CRITIC weighting method and composition changes after processing].

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.

[Molecular Identification on Three Medicinal Phlomis Species Produced in Yunnan Province Based on ITS and matK].

Objective: To analyze ITS region and matK gene sequences of three medicinal Phlomis plants,in order to provide molecular basis for identifying and protecting their wild resources.

Methods: PCR and sequencing were conducted on Phlomis likiangensis,Phlomis melanantha and Phlomis betonicoides wild populations by primers pairs ITS4 / ITS5 and matKXF / matK5 R.

Results: The smallest inter-K2 P genetic distance was further than the largest intra-K2 P genetic distance in Phlomis likiangensis, Phlomis melanantha and Phlomis betonicoides.