Related papers: Protocol for Clustering 4DSTEM Data for Phase Diff…
A class of chalcogenide alloy materials that shows significant changes in optical properties upon an amorphous-to-crystalline phase transition has lead to development of large data capacities in modern optical data storage. Among…
Metal clusters are partway between molecular and bulk systems and thus exhibit special physical and chemical properties. Atoms can rearrange within a cluster to form different structural isomers. Internal degrees of freedom and the…
Engineering phase change materials (PCM) to realize superior data storage devices requires a detailed understanding of crystallization kinetics and its temperature dependence. The temperature dependence of crystallization differs distinctly…
Phase-change materials (PCMs) can switch between different crystalline states as a function of an external bias, offering a pronounced change of their dielectric function. In order to take full advantage of these features for active…
The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However,…
A computational approach by an implementation of the Principle Component Analysis (PCA) with K-means and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify structural and dynamical heterogeneities of…
One of the most widely used active materials for phase-change memories (PCM), the ternary stoichiometric compound Ge$_2$Sb$_2$Te$_5$ (GST), has a low crystallization temperature of around 150$^\circ$C. One solution to achieve higher…
Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass…
The optical properties of phase-change materials (PCM) can be tuned to multiple levels by controlling the transition between their amorphous and crystalline phases. In multi-material PCM structures, the number of discrete reflectance levels…
Chalcogenide phase-change materials (PCMs) show a significant contrast in optical reflectivity and electrical resistivity upon crystallization from the amorphous phase and are leading candidates for non-volatile photonic and electronic…
We analyze, design and assess the performance of a reflective reconfigurable metasurface (MS) architecture for optical wireless communications. The device is based on the Ge-Sb-Te (GST225) phase-change material (PCM) alloy, thermally…
Tailoring the degree of structural disorder in Ge-Sb-Te alloys is important for the development of non-volatile phase-change memory and neuro-inspired computing. Upon crystallization from the amorphous phase, these alloys form a cubic…
Chalcogenide phase-change materials (PCMs) are regarded as the leading candidate for storage-class non-volatile memory and neuro-inspired computing. Recently, using the $TiTe_2$/$Sb_2Te_3$ material combination, a new framework -…
Structural colors generated due to light scattering from static all-dielectric metasurfaces have successfully enabled high-resolution, high-saturation and wide-gamut color printing applications. Despite recent advances, most demonstrations…
Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability…
Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, traditional methods of visual…
A computational approach via implementation of the Principle Component Analysis (PCA) and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify domain structures of supercooled liquids is developed. Raw…
The association of scanning transmission electron microscopy (STEM) and the detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of…
Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to…
The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we…