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

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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.