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:

Past interests have included magnetic-ferroelectric multiferroic materials, magnetism in magnetically dilute systems, and strain driven structural properties.

Technical Skills

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
red text: recently used
Cloud of research keywords. Red text means more recently used.