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

非技术性描述：分子界面是两个不同的分子物质区域相遇的空间。分子界面形成有机电子器件的有源区，例如发光二极管、太阳能电池和晶体管。这些接口的结构和由此产生的属性决定了它们的功能，从而决定了设备性能。几十年来，有机电子器件一直基于无序薄膜。对于基于具有外延界面的高度有序的结晶膜的无机器件，情况正好相反，其中晶体基质由于其上级电子性质而在界面上是连续的。本研究将探索有机外延界面作为高性能有机电子和光子器件的新范例。本计画将发展预测分子界面结构与性质的计算工具。模拟将通知候选材料的外延生长和器件制造的选择。这项工作将开辟有机电子领域的新方向，并为更高效的器件和混合有机-无机集成光子学提供新的材料平台。它将超越当今有机外延的试错法，通过整合量子力学模拟的基本原理、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。技术描述：这项研究旨在填补有机电子领域的空白，其中实验理解不足，计算工具几乎不存在。它将推进分子间相互作用的基本理解，支配有机和无机基板上的分子晶体的外延生长。这些知识将为模型的开发提供信息，这些模型可以基于结合机器学习的第一原理模拟来预测具有目标光学和/或电子特性的实验可行的异质结构。将实施一种新的方法来预测低吞吐量实验的结果，通过机器学习模型训练数据，用于在卡内基梅隆大学云实验室设施的高吞吐量实验中测量的替代描述符。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算建模和机器学习算法中，以提高其准确性。该项目将以展示基于外延有机界面的新器件技术为高潮，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建议在该项目中开发的算法以开源方式实现，并行代码与下一代超级计算架构兼容，并将由此产生的数据集公开提供。此外，该团队将为研究生和本科生创造教育机会，并为K-12学生创造外展机会。该项目旨在通过技术和劳动力开发来提高美国在全球半导体行业中的竞争力。该奖项反映了NSF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。

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.