Classical-scaling methods for non-adiabatic quantum dynamics
Project ID: 2531ad1505
(You will need this ID for your application)
Research Theme: Physical Sciences
UCL Lead department: Chemistry
Lead Supervisor: Tim Hele
Project Summary:
Solving the world’s energy and climate challenges will require new technologies that can efficiently harvest, store, convert and use energy, and many of these processes involve electrons ‘jumping’ between molecules or atoms. These “non-adiabatic” processes involve the coupled quantum mechanical motion of nuclei and electrons and simulating them is extremely challenging. The goal of this project is to derive a non-adiabatic quantum dynamics method which (a) accurately captures the oscillations between electronic states, (b) conserves the quantum Boltzmann distribution, (c) scales classically with system size and (d) has a derivation from first principles. To our knowledge there is no known method satisfying all four of these criteria, and deriving such a method would be a landmark breakthrough in non-adiabatic quantum dynamics. The Hele group has already made significant progress towards a derivation and this studentship is an opportunity to continue this promising research.
In this challenging and rewarding project you will be working mainly with Dr Tim Hele and his group, though there is scope for collaboration within and outside UCL. At the start you will benefit from guidance from a senior PhD student and later on in your degree there is scope for mentoring Master’s and junior PhD students. Dr Tim Hele and his group have a track record of winning awards for deriving accurate and fast quantum mechanics methods.
This project will involve searching and analysing the literature, algebraic derivation and coding, including calculating small test cases and also running large jobs on high-performance computing clusters.
We are looking for a talented and ambitious student with expertise in theoretical chemistry, theoretical physics or applied mathematics. Knowledge of quantum mechanics is highly desirable, as is expertise in coding (especially python) and an excellent academic track record. Experience of a computational or theoretical research project is also desirable.