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

在过去的十年中，杂化有机-无机钙钛矿（HOIP）由于其对于溶液处理的材料的特殊半导体性质而被大量研究，例如尖锐的带边、高发光产率和长程电荷输运。金属卤化物钙钛矿由化学计量ABX 3的有机分子单价阳离子（A）、金属（B）和卤化物（X）组成。阳离子的变化通过减少非辐射损失来提高发光产率。其2D钙钛矿类似物的变体-胶体纳米颗粒（NP）钙钛矿，例如纳米晶体或2D纳米片-承诺增加的稳定性，高量子产率，也在LED中的低激发密度下，以及来自1D/2D限制的强激子限制。该项目旨在将联合收割机机器学习（ML）和从头算方法结合到材料和实验数据中，并结合钙钛矿合成的进展，对钙钛矿的光电应用进行全面的研究。博士项目的第一个分支是实现一种新型的机器学习模型，该模型考虑了当前钙钛矿面临的固有不确定性和可重复性问题。其关键思想是从少量的实验数据开始构建一个粗略的贝叶斯机器学习模型;然后，该模型基于探索未知组合空间和利用可能是最佳组合之间的平衡，建议合成和测试哪些组合，并将实验结果反馈到模型中，以便模型建议要探索的新组合。这种迭代的主动学习方法通过使搜索偏离可能是死胡同的组合空间来避免组合搜索。贝叶斯优化已经在数学文献中提出，但在材料科学中的应用是有限的。该模型已被证明是成功的一组玩具问题，并接受机器学习会议研讨会作进一步讨论。该模型的真正基准测试将使用从合作者收集的真实的实验数据和从文献收集的数据进行。博士项目的第二个分支旨在对3D和2D钙钛矿系统的物理学进行全面的从头算研究。已知钙钛矿表现出具有许多不同稳定多晶型物的相复杂性。这些多晶型物可以在不同的温度和压力范围下存在。我们的目标是研究钙钛矿系统的结构和稳定性：2D或3D系统中的相行为是什么？究竟是什么影响了这些系统的相位稳定性？缺陷的形成和浓度如何影响相的形成？界面自由能在HOIP体系的终相环境中起作用吗？它的目的是研究这些问题，使用两种方法，密度泛函理论（DFT）和ML力场。首先，使用DFT研究各种不同的钙钛矿环境将产生高精度的能量和力场。为了解决缺陷形成的问题，在室温下单独使用DFT进行模拟将非常昂贵。使用ML力场，可以受益于数量级更便宜的计算成本，以及与DFT相似的精度。通过创建大量的晶胞，其中唯一的变化是有机阳离子的有序度，并对这些细胞进行DFT模拟，从完全有序到完全无序，调查的目的是获得这些有序和无序晶胞之间的能量比较，以及是否可以最终表明，这种有序性可以影响系统的带隙。如果是这样的话，那么未来的实验工作将不得不警惕这些有序效应，并在合成过程中控制它，以允许带隙的另一个微调旋钮。

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.