- Ralph Smith (Mathematics, NCSU)
- Kimberly Weems (Statistics, NC Central University)
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 and heat resistance scanning transmission electron microscopy (STEM) has revolutionized this process by providing direct observations of the atoms that form a crystalline material. The image data are noisy and thus, statistical inverse modeling is needed to estimate material properties and quantify uncertainty .
Objectives: To use Bayesian hierarchical modeling to extract information about material properties from images. This project focuses 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.
References: Fancher, C., et al., Use of Bayesian inference in crystallographic structure refinement via full diffraction profile analysis. Sci Rep, 2016. 6: p. 31625.