Country of origin: Hong Kong
Subject: Chemistry
Matriculation year: 2020
My research interest is in computational predictions and analyses of organic reactions. I perform calculations, and often in collaborations with experimentalists, to uncover reaction mechanisms and rationalise the reactivities for organic reactions. Reactions that I have come across include DNA functionalisation reactions and stereoselective reactions with diarylprolinol silyl ether catalysts (Org. Chem. Front., 2022,9, 3730-3738).
My PhD research also involves methodology developments, which aim to set up a more efficient framework and automate the procedure for explaining reactivities and deriving predictive models. One of the aspects is the exploration of the conformational space, i.e. the possible conformations a molecule can take up by rotations via single bonds and the associated energies for adopting the conformations. Features of conformational space are often important for rationalising the reactivity of a molecule. To tackle the challenge of exploring the conformational space thoroughly and efficiently, I have developed the CONFPASS Python package (https://github.com/Goodman-lab/CONFPASS). CONFPASS offers rapid analyses to guide conformer re-optimisations of force field structures at the density functional theory level for organic molecules. The technology is based on clustering algorithms and machine learning classification models. At the moment, I am exploring the potential of deep learning and neural network to speed up the process of deriving organic mechanisms.
Previous degree: MSc Chemistry at Imperial College London