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.

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. Here, aiming at the visualization of Convolutional Neural Network-based models toward the qualitative and quantitative analysis of 1D spectroscopy, we developed a novel visualization algorithm (1D Grad-CAM) to more accurately display the decision-making process of the CNN-based models. Different from the classical Grad-CAM, with the removal of the gradient averaging (GAP) and the ReLU operations, a significantly improved correlation between the gradient and the spectral location and a more comprehensive spectral feature capture were realized for 1D Grad-CAM. Furthermore, the introduction of difference (purity or linearity) and feature contribute in the CNN output in 1D Grad-CAM achieved a reliable evaluation of the qualitative accuracy and quantitative precision of CNN-based models. Facing the qualitative and adulteration quantitative analysis of vegetable oils by the combination of Raman spectroscopy and ResNet, the visualization by 1D Grad-CAM well reflected the origin of the high accuracy and precision brought by ResNet. In general, 1D Grad-CAM provides a clear vision about the judgment criterion of CNN and paves the way for CNN to a broad application in the field of 1D spectroscopy.

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. The shape factor was introduced to monitor their shape changes, and a common neighbor analysis was employed to characterize the local structures of atoms. The results reveal that both the thermal and shape stabilities of these nanorings can be remarkably improved by decreasing the inner radius. Furthermore, they all experienced the evolutionary process from ring to pie and spherelike morphologies, finally resulting in structural collapse. The stacking faults were observed in these rings during the heating process. Our work sheds light on the fundamental understanding of alloyed nanorings subjected to heating, hence offering a theoretical foundation for their syntheses and applications.

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. Taking (PtNi)N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)38 clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)55 clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.

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. In this study, comparative transcriptomic analyses were performed using a near-isogenic CMS line 14A (CMS-14A) and a maintainer line 14B (ML-14B) as experimental materials. A total of 17,349 differentially expressed genes were detected between CMS-14A and ML-14B at the PMC meiosis stage. Among them, six unigenes associated with CMS and 108 unigenes involved in energy metabolism were identified. The gene orf165 was found in CMS-14A. When orf165 was introduced into ML-14B, almost 30% of transgenic plants were CMS. In addition, orf165 expression in transgenic CMS plants resulted in abnormal function of some genes involved in energy metabolism. When orf165 in transgenic CMS plant was silenced, the resulted orf165-silenced plant was male fertile and the expression patterns of some genes associated with energy metabolism were similar to ML-14B. Thus, we confirmed that orf165 influenced CMS in pepper.

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. The stacking faults appear in both Pt and Au shells despite different structures of the Co core. The Co core and Pt shell concurrently melt and present an identical melting point in both Co-Pt core-shell nanoparticles. However, typical two-stage melting occurs in both Co-Au core-shell nanoparticles. Furthermore, the Au shell in the hcp-Co-core/fcc-Au-shell nanoparticle exhibits a lower melting point than that in the fcc-Co-core/fcc-Au-shell one, while the melting points are closely equal for both hcp and fcc Co cores. All of these observations suggest that their thermal stability strongly depends on the structure of the core and the element of the shell.

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 Pt3Co and PtCo systems, and Pt3Co-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. In contrast, Co-Pt core-shell nanoparticles display the worst thermal stability compared with the aforementioned nanoparticles. Furthermore, their melting initiates in the core and extends outward surface, showing a typical two-stage melting mode. The solid-solid phase transition is discovered in Co core before its melting. This work demonstrates the importance of composition distribution to tuning the properties of binary nanoparticles.

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.e., body-centered cubic (bcc), face-centered cubic (fcc), and amorphous phases, has been considered. Our results show that Pt-Fe alloy is the most stable configuration among the four types of bimetallic nanoparticles. It has been discovered that the amorphous Fe cannot stably exist in the core and preferentially transforms into the fcc phase. The phase transition from bcc to hexagonal close packed (hcp) has also been observed in bcc-Fe-core/Pt-shell nanoparticles. In contrast, Fe with the fcc structure is the most preferred as the core component. These findings are helpful for understanding the structure-property relationships of Pt-Fe bimetallic nanoparticles, and are also of significance to the synthesis and application of noble metal based nanoparticle catalysts.

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. Our investigation reveals that for hollow Pt-core/Au-shell nanoparticle, premelting originates at the exterior surface, and a typical two-stage melting behavior is exhibited, similar to the solid ones. However, since the interior surface provides facilitation for the premelting initiating at the core, the two-stage melting is also observed in hollow Au-core/Pt-shell nanoparticle, remarkably different from the solid one. Furthermore, the collapse of hollow structure is accompanied with the overall melting of the hollow Pt-core/Au-shell nanoparticle while it occurs prior to that of the hollow Au-core/Pt-shell nanoparticle and leads to the formation of a liquid-core/solid-shell structure, although both of them finally transform into a mixing alloy with Au-dominated surface. Additionally, the existence of stacking faults in the hollow Pt-core/Au-shell nanoparticle distinctly lowers its melting point. This study could be of great importance to the design and development of novel nanocatalysts with both high activity and excellent stability.

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. The melting of all the nanoparticles initiates at surface and gradually spreads into the core. Due to the lattice mismatch among Au, Cu and Pt, stacking faults have been observed in the shell and their numbers are associated with the Cu/Pt ratios. With the increasing temperature, they have reduced continuously for the Cu-dominated shell while more stacking faults have been produced for the Pt-dominated shell because of the significantly different thermal expansion coefficients of the three metals. Beyond the overall melting, all nanoparticles transform into a trimetallic mixing alloy coated by an Au-dominated surface. This work provides a fundamental perspective on the thermodynamic behaviors of trimetallic, even multimetallic, nanoparticles at the atomistic level, indicating that controlling the alloy composition is an effective strategy to realize tunable thermal stability of metallic nanocatalysts.

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. In addition, improper gridding, which is influenced by both the misalignment and high noise level, will affect the high throughput analysis. In this paper, we have presented a fully automatic gridding technique to break through the limitation of traditional mathematical morphology gridding methods. First, a preprocessing algorithm was applied for noise reduction. Subsequently, the optimal threshold was gained by using the improved Otsu method to actually locate each spot. In order to diminish the error, the original gridding result was optimized according to the heuristic techniques by estimating the distribution of the spots. Intensive experiments on six different data sets indicate that our method is superior to the traditional morphology one and is robust in the presence of noise. More importantly, the algorithm involved in our method is simple. Furthermore, human intervention and parameters presetting are unnecessary when the algorithm is applied in different types of microarray images.

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). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.