AUTHOR=Abroshan Hadi , Kwak H. Shaun , An Yuling , Brown Christopher , Chandrasekaran Anand , Winget Paul , Halls Mathew D. TITLE=Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics JOURNAL=Frontiers in Chemistry VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2021.800371 DOI=10.3389/fchem.2021.800371 ISSN=2296-2646 ABSTRACT=

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials’ chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.