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While the microscopic structure of defected solid crystalline materials has significant impact on their physical properties, efficient and accurate determination of a given polycrystalline microstructure remains a challenge. In this paper…
Materials identification and structural understanding from powder X-ray diffraction (PXRD) data is a long-standing challenge in materials science, fundamental to discovering and characterizing novel materials. A prerequisite for full…
Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals.…
Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitative step of…
Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the…
Sufficient dimension reduction (SDR) is continuing an active research field nowadays for high dimensional data. It aims to estimate the central subspace (CS) without making distributional assumption. To overcome the large-$p$-small-$n$…
Solving crystal structures from powder X-ray diffraction (XRD) is a central challenge in materials characterization. In this work, we study the powder XRD-to-structure mapping using gradient descent optimization, with the goal of recovering…
To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray…
Manufacturing of particulate products across many industries relies on accurate measurements of particle size distributions in dispersions or powders. Laser diffraction (or small angle light scattering) is commonly used, usually off-line,…
Statisticians increasingly face the problem to reconsider the adaptability of classical inference techniques. In particular, divers types of high-dimensional data structures are observed in various research areas; disclosing the boundaries…
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of…
X-ray diffraction with high spatial resolution is commonly used to characterize (poly-)crystalline samples with, for example, respect to local strain, residual stress, grain boundaries and texture. However, the investigation of highly…
Detailed investigation of static light scattering $(SLS) $ has been attempted in this work using dilute water dispersions of homogenous spherical particles, poly($N$-isopropylacrylamide) microgels and simulated data. When Rayleigh-Gan-Debye…
For small samples, the modification of the XRR profile by the geometrical factors manifesting due to profile and size of the beam and the size of the sample is significant. Geometrical factors extend till spill over angle which is often…
Analysis of measured X-ray diffraction (XRD) data from heterostructures with fitting analysis is discussed, for which computer program was written. Lattice constant and Poisson's ratio of a multi-compound layer is calculated from Vegard's…
Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is ubiquitous across many scientific disciplines, including…
In the present work, the microcrystalline characteristics of KBr thin films have been investigated by evaluating the breadth of diffraction peak. The Williamson-Hall, the Size-Strain Plot and the single line Voigt methods are employed to…
The spectral dispersion of light is critical in applications ranging from spectroscopy to sensing and optical communication technologies. We demonstrate that ultra-high spectral dispersion can be achieved with a finite-size surface plasmon…
Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search…
Strain and defects in crystalline materials are responsible for the distinct mechanical, electric and magnetic properties of a desired material, making their study an essential task in material characterization, fabrication and design.…