NSF Award
2323749
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.<br/><br/>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.<br/><br/>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.
