I am a student at the University of the Pacific studying physics and data science. My current research focus and interests are computational materials science and machine learning.
Developed and ran computational workflows to generate and relax thousands of Fe-C crystal structures as training data for machine learning interatomic potentials, using VASP on HPC clusters. Contributed to Python scripts for structure generation, data processing, and model validation against reference values from literature. Reviewed primary literature and collaborated with faculty to make decisions on training parameters, including cutoff radius and model complexity level. Validated trained potentials by analyzing energy-volume curves and elastic constants across multiple Fe-C compositions.
List your projects here.
Anything else you'd like to include.
Email: t_follett[at]u[dot]pacific[dot]edu