Collaborative Research: DMREF: Informed Design of Epitaxial Organic Electronics and Photonics

合作研究：DMREF：外延有机电子和光子学的知情设计

• 批准号: 2323751
• 负责人: Barry Rand
• 金额: $ 48万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323751&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Informed; Design

Non-technical Description: A molecular interface is a space where two different regions of molecular matter meet. Molecular interfaces form the active regions of organic electronic devices, such as light-emitting diodes, solar cells, and transistors. The structure and resulting properties of these interfaces determine their functionality and, thus, device performance. For decades, organic electronic devices have been based on disordered films. The opposite is true for inorganic devices, which are based on highly ordered crystalline films with epitaxial interfaces, where the crystal matrix is continuous across the interface because of their superior electronic properties. This research will explore organic epitaxial interfaces as a new paradigm for high-performance organic electronic and photonic devices. This project will develop computational tools to predict molecular interface structure and properties. Simulations will inform the selection of candidate materials for epitaxial growth and device fabrication. This work will open up a new direction in the field of organic electronics and deliver a new materials platform for more efficient devices and hybrid organic-on-inorganic integrated photonics. It will go beyond today’s trial-and-error approach to organic epitaxy by integrating first principles of quantum mechanical simulations, predictive machine learning algorithms, and experiments to validate and inform the models in a tightly coupled feedback loop.Technical Description: This research aims to fill a void in organic electronics where experimental understanding is scant and computational tools are virtually non-existent. It will advance a fundamental understanding of intermolecular interactions that govern the epitaxial growth of molecular crystals on both organic and inorganic substrates. This knowledge will inform the development of models that can predict experimentally-feasible hetero-structures with targeted optical and/or electronic properties based on first principles simulations combined with machine learning. A new approach will be implemented to predict the outcomes of low-throughput experiments by machine-learned models trained on data for surrogate descriptors measured by high-throughput experiments at Carnegie Mellon University’s Cloud Lab facility. The predicted hetero-structures will be grown via vacuum thermal evaporation and used for device fabrication. The results of the experiments will feed back into the ab initio modeling and machine learning algorithms to hone their accuracy. The project will culminate with the demonstration of new device technologies based on epitaxial organic interfaces, including more efficient organic solar cells, high-performance transistors, and integrated photonics. The PIs propose to make algorithms developed within this project to be implemented in open source, parallel codes compatible with next-generation supercomputing architectures, and the resulting datasets made publicly available. In addition, the team will create educational opportunities for graduate and undergraduate students and outreach opportunities for K-12 students. This project intends to promote US competitiveness in the global semiconductor industry through technology and workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术描述：分子界面是两个不同区域的分子物质相遇的空间。分子界面形成有机电子器件的有源区域，如发光二极管、太阳能电池和晶体管。这些接口的结构和产生的属性决定了它们的功能，从而决定了设备的性能。几十年来，有机电子器件都是基于无序薄膜。无机器件的情况正好相反，它们基于具有外延界面的高度有序的晶体薄膜，由于其优越的电子性能，晶体基质在界面上是连续的。本研究将探索有机外延接口作为高性能有机电子和光子器件的新范例。该项目将开发计算工具来预测分子界面结构和性质。模拟将为外延生长和器件制造的候选材料的选择提供信息。这项工作将为有机电子领域开辟一个新的方向，并为更高效的器件和混合有机-无机集成光子学提供一个新的材料平台。通过整合量子力学模拟的第一原理、预测机器学习算法和实验，在紧密耦合的反馈回路中验证和告知模型，它将超越今天的有机外延试错方法。技术描述：本研究旨在填补有机电子学中实验理解不足和计算工具几乎不存在的空白。它将促进对控制分子晶体在有机和无机衬底上外延生长的分子间相互作用的基本理解。这些知识将为基于第一性原理模拟和机器学习相结合的模型的开发提供信息，这些模型可以预测具有目标光学和/或电子特性的实验可行的异质结构。将采用一种新的方法，通过机器学习模型对卡内基梅隆大学云实验室设施高通量实验测量的代理描述符数据进行训练，来预测低通量实验的结果。预测的异质结构将通过真空热蒸发生长并用于器件制造。实验结果将反馈到从头开始建模和机器学习算法中，以提高其准确性。该项目最终将展示基于外延有机界面的新器件技术，包括更高效的有机太阳能电池、高性能晶体管和集成光子学。pi建议在这个项目中开发的算法以开源的方式实现，与下一代超级计算架构兼容的并行代码，并使结果数据集公开可用。此外，该团队将为研究生和本科生创造教育机会，并为K-12学生创造拓展机会。该项目旨在通过技术和劳动力发展提升美国在全球半导体行业的竞争力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。