Data Science Commons^™

Machine Learning Prediction Of Photoluminescence In Mos2: Challenges In Data Acquisition And A Solution Via Improved Crystal Synthesis, Ethan Swonger, John Mann, Jared Horstmann, Daniel Yang

Seaver College Research And Scholarly Achievement Symposium

Transition metal dichalcogenides (TMDCs) like molybdenum disulfide (MoS2) possess unique electronic and optical properties, making them promising materials for nanotechnology. Photoluminescence (PL) is a key indicator of MoS2 crystal quality. This study aimed to develop a machine-learning model capable of predicting the peak PL wavelength of single MoS2 crystals based on micrograph analysis. Our limited ability to consistently synthesize high-quality MoS2 crystals hampered our ability to create a large set of training data. The project focus shifted towards improving MoS2 crystal synthesis to generate improved training data. We implemented a novel approach utilizing low-pressure chemical vapor deposition (LPCVD) combined with …

Revealing The Three-Dimensional Magnetic Texture With Machine Learning Models, Shihua Zhao

Dissertations, Theses, and Capstone Projects

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.

We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, …

Pyseg: A Python Package For 2d Material Flake Localization, Segmentation, And Thickness Prediction, Diana B. Horangic

Student Research Projects

Thin materials are of interest for their extraordinary physical, mechanical, thermal, electrical, and optical properties. Monolayers and bilayers of 2D materials can be manufactured through a variety of exfoliation methods. To determine layer thickness, Raman spectroscopy or other methods like Rayleigh scattering are used. These methods are, however, slow, and they require equipment beyond an optical microscope. A Python package that automates flake identification processes was built, with access solely to RGB data from an optical microscope assumed. My package, pyseg, localizes flakes on a substrate and then makes a rough estimate of their thickness from first principles. It can …

Topic Modeling And Cultural Nature Of Citations, Marie Coraline Dumaz

Graduate Theses, Dissertations, and Problem Reports

Ever since the beginning of research journals, the number of academic publications has been increasing steadily. Nowadays, especially, with the new importance of online open-access journals and databases, research papers are more easily available to read and share. It also becomes harder to keep up with novelties and grasp an idea of the general impact of a given researcher, institution, journal, or field. For this reason, different bibliometric indicators are now routinely used to classify and evaluate the impact or significance of individual researchers, conferences, journals, or entire scientific communities. In this thesis, we provide tools to study trends in …

Identifying Structure Transitions Using Machine Learning Methods, Nicholas Walker

LSU Doctoral Dissertations

Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as …