A new method for identifying potential hazardous areas of heavy metal pollution in sediments.

Nanoclusters are remarkably promising for the capture and activation of small molecules for fuel production or as precursors for other chemicals of high commercial value. Since this process occurs under a wide variety of experimental conditions, an improved atomistic understanding of the stability and phase transitions of these systems will be key to the development of successful technological applications. In this work, we proposed a theoretical framework to explore the potential energy surface and configuration space of nanoclusters to map the most important morphologies presented by those systems and the phase transitions between them. A fully automated process was developed, which combines global optimization techniques, classical molecular dynamics, and unsupervised machine learning algorithms. To showcase these capabilities of the approach, we explored the example of copper nanoclusters (Cun) where n = 13, 38, 55, 75, 98, 102, and 147. We not only reported a graphical potential energy surface for each size, but also explored the topology of the configuration space via structural and thermodynamic analyses. The effect of size on the potential energy surface and the critical temperature for solid-liquid phase transitions were also reported, highlighting the impact of magic numbers on those quantities.

Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of Finite-Size Particles

With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media with traditional media and provides a new marketing model for traditional media. The results show that the accuracy of the improved k-means algorithm finally reaches about 93%, which is much higher than that of similar algorithms. It can be seen that the improved k-means algorithm has better performance. In the application experiment, this study can effectively find the new media activities with the highest user preference, and the impact of the two websites' application of precision marketing system on users has also increased. It can be seen that the precision marketing system designed this time is more effective.

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Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining

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.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters

Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science with accelerated, high-efficiency discoveries in design, synthesis, manufacturing, characterization and application of novel functional materials, especially at the nanometre scale. The reason is the time efficiency, prediction accuracy and good generalization abilities, which gradually replaces the traditional experimental or computational work. With enormous potentiality to tackle more real-world problems, ML provides a more comprehensive insight into combinations with molecules/materials under the fundamental procedures for constructing ML models, like predicting properties or functionalities from given parameters, nanoarchitecture design and generating specific models for other purposes. The key to the advances in nanomaterials discovery is how input fingerprints and output values can be linked quantitatively. Finally, some great opportunities and technical challenges are concluded in this fantastic field.

Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models.

Here, we report density functional theory calculations combined with the k-means clustering algorithm and the Spearman rank correlation analysis to investigate the stability mechanisms of eight-atom binary metal AB clusters, where A and B are Fe, Co, Ni, Cu, Ga, Al, and Zn (7 unary and 21 binary clusters). Based on the excess energy analysis, the six most stable binary clusters are NiAl, NiGa, CoAl, FeNi, NiZn, and FeAl, and except for FeNi, their highest energetic stabilities can be explained by the hybridization of the d- and sp-states, which is maximized at the 50% composition, i.e., A4B4. Based on the Spearman correlation analysis, the energetic stability of the binary clusters increases with an increase in the highest occupied molecule orbital-lowest unoccupied molecular orbital (HOMO-LUMO) energy separation, which can be considered as a global descriptor. Furthermore, reducing the total magnetic moment values increases the stability for binary clusters without the Fe, Co, and Ni species, while the binary FeB, CoB, and NiB clusters increase their energetic stability with a decrease in the cluster radius, respectively, i.e., an energetic preference for compact structures.

Felipe Orlando Morais

Papers

Investigation of the Stability Mechanisms of Eight-Atom Binary Metal Clusters Using DFT Calculations and k-means Clustering Algorithm

Theoretical investigation of the stability of A55-B nanoalloys (A, B = Al, Cu, Zn, Ag)

Role of Quantum-size Effects on the Dehydrogenation of CH4 on 3d TMn Clusters: DFT Calculations Combined with Data Mining