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.

1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis.

Being characterized by the self-adaption and high accuracy, the deep learning-based models have been widely applied in the 1D spectroscopy-related field. However, the "black-box" operation and "end-to-end" working style of the deep learning normally bring the low interpretability, where a reliable visualization is highly demanded. Although there are some well-developed visualization methods, such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), for the 2D image data, they cannot correctly reflect the weights of the model when being applied to the 1D spectral data, where the importance of position information is not considered.

Thermally Activated Microstructural Evolution of PtIrCu Alloyed Nanorings: Insights from Molecular Dynamics Simulations.

Nanoalloys have attracted extensive interest from the research and industrial community due to their unique properties. In this work, the thermally activated microstructural evolution and resultant collapse of PtIrCu nanorings were investigated using molecular dynamics simulations. Three PtIrCu nanorings with a fixed outer radius and varied inner radii were addressed to investigate the size effects on their thermal and shape stabilities.

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.

Orf165 is associated with cytoplasmic male sterility in pepper.

Cytoplasmic male sterility (CMS) is a maternally inherited trait that derives from the inability to produce functional pollen in higher plants. CMS results from recombination of the mitochondrial genome. However, understanding of the molecular mechanism of CMS in pepper is limited.

Automatic microarray image segmentation with clustering-based algorithms.

Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means).

Thermal Stability of Co-Pt and Co-Au Core-Shell Structured Nanoparticles: Insights from Molecular Dynamics Simulations.

Co-Pt and Co-Au core-shell nanoparticles were heated by molecular dynamics simulations to investigate their thermal stability. Two core structures, that is, hcp Co and fcc Co, have been addressed. The results demonstrate that the hcp-fcc phase transition happens in the hcp-Co-core/fcc-Pt-shell nanoparticle, while it is absent in the hcp-Co-core/fcc-Au-shell one.

Thermal Stability of Platinum-Cobalt Bimetallic Nanoparticles: Chemically Disordered Alloys, Ordered Intermetallics, and Core-Shell Structures.

Pt-Co bimetallic nanoparticles are promising candidates for Pt-based nanocatalysts and magnetic-storage materials. By using molecular dynamics simulations, we here present a detailed examination on the thermal stabilities of Pt-Co bimetallic nanoparticles with three configurations including chemically disordered alloy, ordered intermetallics, and core-shell structures. It has been revealed that ordered intermetallic nanoparticles possess better structural and thermal stability than disordered alloyed ones for both PtCo and PtCo systems, and PtCo-Pt core-shell nanoparticles exhibit the highest melting points and the best thermal stability among Pt-Co bimetallic nanoparticles, although their meltings all initiate at the surface and evolve inward with increasing temperatures.

Atomic structure and thermal stability of Pt-Fe bimetallic nanoparticles: from alloy to core/shell architectures.

Bimetallic nanoparticles comprising noble metal and non-noble metal have attracted intense interest over the past few decades due to their low cost and significantly enhanced catalytic performances. In this article, we have explored the atomic structure and thermal stability of Pt-Fe alloy and core-shell nanoparticles by molecular dynamics simulations. In Fe-core/Pt-shell nanoparticles, Fe with three different structures, i.

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing.

Diverse melting modes and structural collapse of hollow bimetallic core-shell nanoparticles: a perspective from molecular dynamics simulations.

Introducing hollow structures into metallic nanoparticles has become a promising route to improve their catalytic performances. A fundamental understanding of thermal stability of these novel nanostructures is of significance for their syntheses and applications. In this article, molecular dynamics simulations have been employed to offer insights into the thermodynamic evolution of hollow bimetallic core-shell nanoparticles.

Tunable thermodynamic stability of Au-CuPt core-shell trimetallic nanoparticles by controlling the alloy composition: insights from atomistic simulations.

A microscopic understanding of the thermal stability of metallic core-shell nanoparticles is of importance for their synthesis and ultimately application in catalysis. In this article, molecular dynamics simulations have been employed to investigate the thermodynamic evolution of Au-CuPt core-shell trimetallic nanoparticles with various Cu/Pt ratios during heating processes. Our results show that the thermodynamic stability of these nanoparticles is remarkably enhanced upon rising Pt compositions in the CuPt shell.

Using the maximum between-class variance for automatic gridding of cDNA microarray images.

Gridding is the first and most important step to separate the spots into distinct areas in microarray image analysis. Human intervention is necessary for most gridding methods, even if some so-called fully automatic approaches also need preset parameters. The applicability of these methods is limited in certain domains and will cause variations in the gene expression results.

Improving the computational efficiency of recursive cluster elimination for gene selection.

The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE).