Frontiers in Chemistry (Jan 2022)

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations

  • H. Shaun Kwak,
  • Yuling An,
  • David J. Giesen,
  • Thomas F. Hughes,
  • Christopher T. Brown,
  • Karl Leswing,
  • Hadi Abroshan,
  • Mathew D. Halls

DOI
https://doi.org/10.3389/fchem.2021.800370
Journal volume & issue
Vol. 9

Abstract

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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.

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