Using deep learning, atomistic computer simulations, group theory, and scanning transmission electron microscopy, my work is to understand the fundamental physics of solids in order to design novel materials for energy-related applications.
I focus on oxides in realistic environments: beyond bulk, I investigate materials with surfaces & interfaces, dopants, ionic vacancies, and transport anisotropy. To do so, I employ density functional theory and high-performance computing, crystallography, STEM, and Monte Carlo techniques. I am recently expanding into applying deep learning to physics problems, using neural nets of my design trained on data sets generated from my simulations.
Past projects include:
- ionic transport anisotropy in oxygen deficient Lanthanum Cobaltites via STEM and first principles theory
- phase stability and oxygen transport within Yttria|Ceria superlattices
- magnetism effects on superconductivity in van der Waals coupled monolayers
Past interests have included magnetic-ferroelectric multiferroic materials, magnetism in magnetically dilute systems, and strain driven structural properties.
|Deep Learning||Basics of deep learning, specializing in deep learning for scientific applications|
|Microscopy||Certified operator of ORNL’s NION UltraSTEM 200 Electron Microscope|
|DFT||Decade experience with atomistic ab-initio calculations and modelling|
|Monte Carlo||Adapted Metropolis code to perform study of magnetism of the Aurivillius family|
|Crystallography||Space groups and other mathematical tools|
|Programming||linux & HPC, Python, C, C++, MATLAB, Mathematica|