Next generation photovoltaics by nonadiabatic dynamics

Started

Renewable Energy Sources(Solar Energy, Photovoltaics)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Chemistry)
PHYSICAL SCIENCES AND MATHEMATICS (Physics)
PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials)
PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)

Not Cross-cutting

Dr JM Frost
Chemistry
Imperial College London

Standard

EPSRC

15 July 2024

14 July 2027

36 months

£549,670

Unknown

London

NC : Physical Sciences

Dr JM Frost, Chemistry, Imperial College London

Project Contact, Argyll College
Project Contact, University of Cambridge

The goal of this grant is to develop new organic materials for solar cells that can convert light from red to blue. Done with sufficient quantum efficiency, this would enable a photovoltaic device (solar cell) with a power conversion efficiency of nearly 50%. This compares to a traditional single band gap device with a maximum efficiency of 34%. As these upconverting films are entirely optical, they can be made relatively easily, and the design process of the light management and the combined electrical/optical design of the single-bandgap solar cell can be separated.Our approach is to develop methods to model the processes occurring on a computer. This is challenging, as when you excite a material with a photon, the standard Born-Oppenheimer approximation we use in most quantum mechanics stops working, and we have to consider an exponentially exploding set of entangled quantum states. Also, the molecules that are useful for a technical application are very large (hundreds of atoms). This stretches our ability to solve the equations of quantum mechanics even in the Born-Oppenheimer approximation, on the largest computers.We have two main routes to overcome these limitations:First, we will develop methods and new computer codes that make use of the Feynman path integral approach to quantum mechanics. This alternative formulation has not had much application to materials, mainly because the mathematical objects you need to manipulate are quite abstract. One nice thing about this approach to quantum mechanics is that it can deal with infinite degrees of freedom, with a hierarchy of approximations that can be turned on and off for the problem of interest.Second, we take some of the ideas and techniques in machine learning, and apply them to quantum chemistry. We will develop methods to fit empirical quantum mechanical models to the materials are interested in, considerably reducing the computational burden, while retaining accuracy.Both these approaches are enabled by modern scientific programming languages and agile software development practices from industry - small teams can now implement a large quantity of new methods in a short amount of time.However, modelling will only take you so far. We will also do spectroscopic measurements on molecular upconverting devices, combining them with modelling in order to understand the working mechanism; these measurements will also validate the approximations made in our models. A particular technique we will use is called electron paramagnetic resonance. This is directly sensitive to the quantum mechanical spin of the excited states.Our aim is to develop empirical design rules for how to make higher efficiency devices. These will pass on to our synthetic chemistry collaborators, who will make these new molecules, which we will measure. Closing the loop on modelling, experimental validation, and then design and measurement ensures we are kept deeply in touch with reality, and offers the best route to make progress.

24/07/24