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

通过机器学习驱动的实验实现高效发光器件的高性能二维钙钛矿纳米颗粒

• 批准号: 1944314
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1944314
• 关键词: Realising; performance; 2D; perovskite; nanoparticles

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.

在过去的十年里，有机-无机复合钙钛矿(HOIP)由于其独特的半导体性质，如尖锐的能带、高的发光效率和远距离的电荷传输，在过去的十年中受到了广泛的研究。金属卤化物钙钛矿是由有机分子一价阳离子(A)、金属(B)和卤化物(X)组成的化学计量比ABX3。阳离子的变化通过减少非辐射损失来提高发光产量。它们的2D钙钛矿类似物的变体-胶体纳米颗粒(NPs)钙钛矿，例如纳米晶体或2D纳米小片-承诺提高稳定性，高量子产率，也在LED的低激发密度下，以及1D/2D限制下的强激子限制。该项目旨在结合材料和实验数据的机器学习(ML)和从头算方法，与钙钛矿合成的进展相结合，对光电应用的钙钛矿进行全面的研究。博士项目的第一个分支是实施一种新的机器学习模型，该模型考虑了当前钙钛矿材料所面临的固有的不确定性和可重复性问题。其核心思想是从少量的实验数据出发，建立一个粗略的贝叶斯机器学习模型；该模型在探索未知成分空间和开发可能是最优的成分之间取得平衡的基础上，建议合成和测试哪些成分，并将实验结果反馈到模型中，以建议新的成分进行探索。这种迭代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被提出，但到目前为止在材料科学中的应用是有限的。该模型已经被证明在一组玩具问题上是成功的，并被机器学习会议研讨会接受以供进一步讨论。该模型的真正基准将使用从合作者那里收集的真实实验数据和从文献中收集的数据来进行。博士项目的第二个分支计划是对3D和2D钙钛矿系统的光物理进行全面的从头算研究。众所周知，钙钛矿具有许多不同的稳定晶型，具有复杂的相结构。这些多晶型可以在不同的温度和压力范围内存在。我们的目标是研究钙钛矿体系的结构和稳定性：在2D或3D体系中的相行为是什么？究竟是什么影响了这些体系中的相稳定性？缺陷的形成和浓度如何影响相的形成？界面自由能在HOIP系统的最终相环境中起作用吗？利用密度泛函理论(DFT)和ML力场两种方法对这些问题进行了研究。首先，利用密度泛函理论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，仅用密度泛函理论在室温下进行模拟将是非常昂贵的。使用ML力场，人们可以受益于数量级更低的计算成本，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，并对这些晶胞进行从完全有序到完全无序的DFT模拟，研究的目的是推导出这些有序和无序晶胞之间的能量比较，以及是否可以得出结论，这种有序性可以影响系统的带隙。如果是这样的话，未来的实验工作将不得不警惕这些有序效应，并在合成过程中对其进行控制，以允许对带隙进行另一个微调。