Photovoltaics (PV) allows for the direct conversion of sunlight to electricity. It is one of the most important renewable energy technologies being utilised in our race towards net-zero carbon emissions. While crystalline silicon PV (c-Si PV) is the most efficient and cost-effective technology to date for many applications, there are certain applications in which silicon is simply not applicable due to c-Si being inherently heavy, brittle, and opaque. Alternative PV technologies are thus required. Organic PV (OPV) is one very promising and complimentary technology to c-Si PV due to the contrasting material properties of organic semiconductors in that they are light-weight, flexible and semi-transparent and compatible with high-throughput roll-to-roll processing. OPV also outperforms c-Si PV in low-light conditions. Some of the niche applications of OPV include building integrated PV, portable electronics, internet-of-things, and aerospace.

OPV efficiencies have almost doubled in recent years and are closing in on 20% thanks to recent advances in organic synthesis of so-called non-fullerene electron acceptors. If we are to exceed 20% efficiencies and move closer to the theoretical limit, more work is needed to understand the key molecular and electronic processes at play. We need a deeper understanding of the electronic structure of the molecules in the solid-state and how local inter- and intra-molecular interactions affect electron transfer and transport in the device. Piecing these together will allow for new design rules to be obtained, which will then be applied to produce significant advances in device performance.

In this project, we will utilise a combination of computational and experimental approaches to first understand the current state of the art systems and then develop design rules for achieving record efficiency OPV devices. To this end, the candidate will apply ground-state and time-dependent Density Functional Theory (DFT) methods, molecular dynamics (MD) simulations, and deep learning (DL) algorithms, to assist the experimental development of new organic semiconducting materials. The use of advanced high-throughput and GPU-accelerated supercomputing for multiscale modelling and simulations (integrating DFT and MD across different length scales), and the application of DL neural networks is expected to speed up the development of OPV materials, by narrowing down the design space, unlocking structure-property relationships, and even more, by discovering unexpected molecular designs.