Realising high-performance 2D perovskite nanoparticles for efficient light-emitting devices with machine-learning driven experimentation

Hybrid organic-inorganic perovskites (HOIPs) have been heavily studied, over the past decade, due to their exceptional semiconducting properties for a solution-processed material, such as sharp band edges, high luminescence yields and long-range charge transport. Metal-halide perovskites are composed of an organic molecule monovalent cation (A), a metal (B) and a halide (X) in the stoichiometry, ABX3. Variations of the cation improve luminescence yields by reductions of non-radiative losses. Variants of their 2D perovskite analogues - colloidal nanoparticle (NPs) perovskites, for example nanocrystals or 2D nanoplatelets - promise increased stability, high quantum yields, also at low excitation densities in LEDs, and strong excitonic confinement from 1D/2D confinement. This projects aims to combine machine learning (ML) and ab initio methods for materials and experimental data, with advances in synthesis of perovskites to perform a comprehensive investigation into perovskites for optoelectronic applications.The first branch of the PhD project is to implement a novel machine learning model which takes into account the inherent uncertainty and reproducibility issues that current perovskites face. The key idea is to build a coarse Bayesian machine learning model starting from a small amount of experimental data; the model then suggests which compositions to synthesize and test based on balancing between exploring unknown composition space and exploiting compositions that are likely to be optimal, and the results from the experiments are fed back into the model for the model to suggest new compositions to explore. This iterative active learning methodology avoids combinatorial searching by biasing the search away from composition space that is likely to be a dead end. Bayesian optimisation has been proposed in the mathematics literature but application in materials science is thus far limited. This model has been shown to be successful for a set of toy problems, and accepted to machine learning conference workshops for further discussion. The true benchmarking of the model will occur with real experimental data, collected from collaborators, and data collected from literature. The second branch of the PhD project is to intended to be a comprehensive ab initio investigation into the photophysics of 3D & 2D perovskite systems. It is known that perovskites exhibit phase complexity with many different stable polymorphs. These polymorphs can exist under a different range of temperatures and pressures. We aim to investigate the structure and stability of perovskite systems: What is the phase behaviour in a 2D or 3D system? What exactly affects phase stability in these systems? How does defect formation and concentration affect phase formation? Does the interfacial free energy play a role in the final phase environment of a HOIP system? It aims to investigate these questions using two methods, density functional theory (DFT) and ML force fields. First, using DFT investigations into a variety of different perovskite environments will yield high-accuracy energies and force fields.To tackle the question of defect formation, this would be very costly to simulate at room temperature with DFT alone. Using ML force fields, one can benefit from orders of magnitude cheaper computational cost, and similar accuracy to DFT. By creating a large number of unit cells, in which the only variation is the degree of order in the organic cations, and conducting DFT simulations on these cells ranging from perfectly ordered to complete disorder, the investigation aims to derive a comparison of energies between these ordered and disordered unit cells and whether it can be shown conclusively that this ordering can affect the bandgap of the system. If it does, then future experimental work would have to be vigilant of these ordering effects and control it during synthesis to allow for another fine-tuning knob of the bandgap.

Our vision is to take graphene from a state of raw potential to a point where it can revolutionise flexible, wearable and transparent (opto)electronics, with a manifold return in innovation and exploitation. Such change in the paradigm of device manufacturing may revolutionise the global industry. The importance of graphene was recognised by the 2011 statement of the Chancellor of the Exchequer launching the initiative that lead to the funding of the Cambridge Graphene Centre, where the proposed Graphene Technology CDT will be based. The aim is take graphene and related materials from "the British laboratory" to the "British factory floor". Not only does our vision align with this mandate, but it also exploits and strengthens several key areas of national importance where the UK has recognised excellence, such as printed electronics, energy and RF & Microwave Communications. Thus, we will strive for both economic impact, by stimulating new UK-manufactured high-value products, and societal benefits, by utilising graphene in potentially many areas including security, energy efficiency and quality of life.
The beneficiaries of our proposal will be of course the cohorts of students that will be trained every year, but will extend more widely. Considering the private sector, we have already indentified tens of companies that will benefit from our work. To achieve the final goal of graphene-technology, and to ease the transition to commercialisation, we have strong alignment with industry needs and engage them as project partners of the CDT: Dyson, Novalia, Plastic Logic, Nokia, Toshiba, BAE Systems, Aixtron, PEL, Nanocyl, IdTechEx, Philips, Dupont, CambridgeIP, Polyfect, Agilent, Nippon Kayaku, Victrex, IMEC. Many more are also partnering with the Cambridge Graphene Centre, and even more are expected to join and benefit directly or indirectly from our work. We consider the civilian sectors of healthcare, telecommunications, energy and homeland security to be those in which applications based on graphene can make significant impact on society at large. There are also applications in defence, especially in secure communications and radars. This will foster competitiveness and enhance quality of life. In particular, the proposed CDT will be of prime interest to industries dealing with the following devices and applications: 1. Mobile communications, wireless sensor networks, including wearable devices. 2. Nano-structured materials for light and microwave energy harvesting. 3. Active and reconfigurable microwave, terahertz and optical materials, including advanced antenna applications for radar and communications.
Policy-makers, within international, national, local government will also benefit. If the vision of graphene as the material of the 21st century is fulfilled, there will be a need for its properties, benefits, applications and advantageousness compared to current technology to be known by the relevant public bodies. For example, any new policy on energy saving, or mobile communications may need to include a reference to the benefits, or limitations, of graphene-based devices.
Economic resilience and innovation require post-doctoral researchers and students trained in new areas. We will contribute to increasing the talent pool for the future graphene industry. The proposed doctoral training centre will provide unique training to students in various aspects of graphene technology: from graphene nanotechnology to energy, RF/microwave and (opto)electronics. This will develop many skilled researchers over the project lifetime, who will stimulate the sustainability of UK graphene engineering research and future commercialisation opportunities across a variety of sectors.