Browsing Division of Chemical Sciences by Subject "Machine Learning"

Now showing items 1-2 of 2

Machine learning and density functional theory assisted insights into the mechanical and oxidation properties of nickel-based superalloys

Khatavkar, Nikhil

Due to global warming and increasing fuel costs, there is a constant thrust toward increasing fuel efficiency and reducing the emissions of gas-turbine engines, which are made out of superalloys. New superalloy materials, ...
Nuclear quantum effects in gas-phase systems with large amplitude motions: A study of 2-fluoroethanol, ethylene glycol, and H3O2

Arandhara, Mrinal

This thesis explores the role of nuclear quantum effects in selected systems containing large amplitude motion through path integral simulations. Recent works have explored molecules with such floppy modes and examined ...