AUTHOR=Kwak H. Shaun , An Yuling , Giesen David J. , Hughes Thomas F. , Brown Christopher T. , Leswing Karl , Abroshan Hadi , Halls Mathew D. TITLE=Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations JOURNAL=Frontiers in Chemistry VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2021.800370 DOI=10.3389/fchem.2021.800370 ISSN=2296-2646 ABSTRACT=

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals.