Events in Physics
Sam Brown
Location: PS1.28
Machine Learning for Structure Prediction of Nanotube-Encapsulated Crystals
Nanotube-encapsulated crystals are novel materials with properties very different to those of the bulk crystal, due to the importance of coordination and confinement. We aim to develop computational structure prediction methods for these 'nanowires', to direct experiment toward promising candidates for device applications.
These systems pose challenges to traditional Density Function Theory (DFT) calculations. This talk will present progress toward quantifying these challenges using Linear-Scaling DFT (LS-DFT), and discuss the potential for machine-learned semi-empirical models to enable high-throughput calculations.