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

技术说明：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性能特征，如电荷迁移率。这个合作项目由设计材料革命和工程师我们的未来（DMREF）计划赞助，探索结构-形态关系，然后使用获得的知识采用筛选和迭代设计方法。该项目联合了加州大学洛杉矶分校的计算、合成和设备构建和表征研究实验室，以加速这些材料的开发。一个分层的计算方法的多尺度形态建模的低聚体供体材料正在开发中，使用快速采样和准确的排名技术相结合，使用集成的电子结构计算，晶体结构预测，分子动力学（MD）模拟和电荷传输计算。晶体结构预测方法采样可能的包装安排，并提供初始几何形状的MD模拟的每个安排，高精度加速MD与极化力场可以用来模拟局部化学性质和无序。作为这种方法的一部分，已知的第一原理和电荷传输分析方法预测施主形态影响的性质，如空穴迁移率和体电子结构。从供体和受体亚基的大型库的组合，筛选的低聚物与有前途的电子结构和形态通过位点选择性交叉偶联反应合成。 显微镜和X射线衍射方法用于分析形态，包括由于退火的形态变化，并与理论预测进行比较。光致发光猝灭，瞬态吸收光谱，和电荷提取线性增加电压被用来评估设备的性能。这种紧密的合作努力使实验结果的反馈能够用于候选分子的迭代系统调整和计算方法的改进。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。基于低聚物（小分子）的有机太阳能电池相对于当前的太阳能技术是低成本的，并且近年来在功率效率方面经历了显著的提高。但需要进行重大改进，以提高其商业化潜力。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，以改善材料形态的预测-许多分子在这些设备中结合在一起的方式。其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，通过计算筛选新的高性能器件的性能，以预测用于创建和测试器件的有希望的分子，然后提高这些器件的性能。该项目涉及不同研究团队的研究生，以学习和发展跨学科技能。参与的学生在项目的各个方面获得经验。鼓励下一代科学家从事STEM职业正在通过对本科生的指导和K-12外展来培养。

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.