Prerequisites: Probability, statistical computing.
Outline: A fundamental task in materials science is to understand materials at the atomic level. With this understanding, materials can be engineered with desirable properties such as hardness, heat resistance, etc. Scanning transmission electron microscopy (STEM) has revolutionized this process by providing direct observations of the atoms that form a crystalline material. These image data are noisy and thus, statistical inverse modeling is needed for better estimates of material properties and quantify uncertainty (1).
Research objectives: To use Bayesian hierarchical modeling to extract information about material properties from images. This project focusses on studying the distributions of defects such as atom-column vacancies and displacements using data-fusion methods resolving the images and defect effect models.
Outcomes: Using the proposed methods for a case study quantifying age distribution. Python code will be available to supplement the STEM imaging toolbox.
- Miles P, Pash G, Smith R, Oates W. Bayesian inference and uncertainty propagation using efficient fractional-order viscoelastic models for dielectric elastomers. J Int Mat Syst Str. 2021; 32:486-496.