DMREF: Iterative Theoretical Morphology Prediction, Synthesis, and Characterization of Novel Donor Oligomers for Accelerated Materials Discovery

技术描述：溶液处理的基于低聚物的有机光伏材料由于其低成本潜力，易于制造和可调特性而成为下一代太阳能电池的有希望的候选者。分子结构和材料形态之间的关系严重影响诸如电荷迁移率等性能特征。这个合作项目是由设计材料来革新和工程我们的未来（DMREF）项目赞助的，探索结构-形态关系，然后利用所获得的知识来采用筛选和迭代的设计方法。该项目联合了加州大学洛杉矶分校的计算、合成、设备构建和表征研究实验室，以加速这些材料的开发。一种用于低聚供体材料多尺度形态建模的分层计算方法正在开发中，该方法结合了快速采样和精确排序技术，使用集成电子结构计算、晶体结构预测、分子动力学（MD）模拟和电荷输运计算。晶体结构预测方法对可能的排列排列进行采样，并为每种排列排列提供初始几何形状，在此基础上，可以利用具有极化力场的高精度加速MD模拟局部化学性质和无序性。作为该方法的一部分，已知的第一性原理和电荷输运分析方法可以预测供体形态影响的性质，如空穴迁移率和体电子结构。从大型供体和受体亚基库的组合中，通过选择性交叉偶联反应合成具有有前途的电子结构和形态的低聚物。显微镜和x射线衍射方法用于分析形貌，包括由于退火引起的形貌变化，并与理论预测进行比较。采用光致发光猝灭、瞬态吸收光谱和线性增加电压的电荷提取来评估器件性能。这种紧密结合的协作努力使实验结果的反馈用于候选分子的迭代系统调谐和计算方法的改进。非技术描述：对清洁和负担得起的能源的需求要求开发和改进替代能源。太阳为地球提供的能源是目前世界能源消耗的9000倍，这使得太阳能成为一个有吸引力的选择。低聚物（小分子）基有机太阳能电池相对于目前的太阳能技术成本低，近年来在功率效率方面有了显著的提高。但要提高其商业化潜力，还需要进行重大改进。在一个合作项目中，加州大学洛杉矶分校的计算、合成和设备表征研究实验室正在开发新的方法和模型，以改进这些设备中材料形态（许多分子结合在一起的方式）的预测。目标是预测基于低聚物的有机太阳能电池的性能，并加速新材料的发现。从大量的候选分子库中，计算筛选新的高性能设备的性能，以预测用于创建和测试设备的有前途的分子，然后提高这些设备的性能。该项目涉及研究生在不同的研究团队学习和发展跨学科的技能。参与的学生在项目的各个方面获得经验。通过对本科生的指导和K-12外展，鼓励下一代科学家从事STEM职业。

Technical Description: Solution-processed oligomer-based organic photovoltaic materials are promising candidates for next-generation solar cells due to their low-cost potential, ease of fabrication, and tunable characteristics. The relationship between molecular structure and materials morphology critically affects performance characteristics such as charge mobility. This collaborative project, sponsored by the Designing Materials to Revolutionize and Engineer Our Future (DMREF) program, explores the structure-morphology relationship, and then uses knowledge gained to employ a screening and iterative design approach. The project unites computational, synthetic, and device construction and characterization research labs at UCLA to accelerate development of these materials. A tiered computational approach for multiscale morphology modeling of oligomeric donor material is being developed, using a combination of rapid sampling and accurate ranking techniques using integrated electronic structure calculations, crystal structure prediction, molecular dynamics (MD) simulations, and charge transport calculations. Crystal structure prediction methods sample possible packing arrangements and provide initial geometries for MD simulations of each arrangement, upon which high-accuracy accelerated MD with a polarizable force field can be employed to simulate local chemical properties and disorder. As part of this approach, known first-principles and charge transport analysis methods predict donor morphology-influenced properties such as hole mobility and bulk electronic structure. From combinations of large libraries of donor and acceptor subunits, screened oligomers with promising electronic structures and morphologies are synthesized via site-selective cross-coupling reactions. Microscopy and x-ray diffraction methods are used to analyze morphologies, including morphology changes due to annealing, and to compare to theoretical predictions. Photoluminescence quenching, transient absorption spectroscopy, and charge extraction by linearly increasing voltage are used to assess device performance. This tightly knit collaborative effort enables feedback from experimental results to be used for iterative systematic tuning of candidate molecules and improvements of computational methods.Non-technical Description: The need for clean and affordable energy demands the development and improvement of alternative energy sources. The sun supplies the Earth with 9000 times the world's current energy consumption, making solar power an attractive option. Oligomer (small molecule)-based organic solar cells are low cost relative to the current solar technology and have experienced significant increases in power efficiency in recent years. But major improvements are needed to enhance its commercialization potential. In a collaborative project, computational, synthetic, and device characterization research labs at UCLA are developing new methods and models to improve prediction of materials morphology - the way in which many molecules fit together - in these devices. The goal is to predict the performance of oligomer-based organic solar cell performance and to accelerate the discovery of new materials. From vast libraries of candidate molecules, the performance of new high-performance devices is screened computationally to predict promising molecules to be used to create and test devices and then improve performance of these devices. This project involves graduate students in diverse research teams to learn and develop interdisciplinary skills. Students involved gain experience in each aspect of the project. Encouragement of the next generation of scientists to engage in STEM careers is being fostered through mentorship of undergraduate students and through K-12 outreach.