Optimal Transport in Quantum Dynamics and Chemistry

In the last 30 years, the theory of optimal transportation has emerged as a fertile field of inquiry, and a diverse tool for exploring applications within and beyond mathematics, in such diverse fields as economics, meteorology, geometry and engineering. More recently, it has empowers today’s machine learning research and become one of the most emerging topics to learn.

This project explores a novel avenue, by developing mathematical and machine learning methods based on optimal transport to overcome computational bottlenecks in chemical dynamics. Specifically, we aim to design new computational algorithms that can incorporate key quantum effects and accurately predict spectroscopic properties of molecules at close to classical computational costs.

Our research lies at the boundary between mathematics, physics, chemistry and machine-learning. It combines Augusto Gerolin’s expertise in Optimal Transport and Machine Learning, David Manolopoulos’s expertize in chemical dynamics, with Annina Lieberherr’s expertise in quasicentroid molecular dynamics.

Faculty Supervisor:

Augusto Gerolin

Student:

Partner:

University of Oxford

Discipline:

Physics

Sector:

Artificial Intelligence; Quantum Science

University:

University of Ottawa

Program: