Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics

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 reli...

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Bibliographic Details
Main Authors: Hadi Abroshan, H. Shaun Kwak, Yuling An, Christopher Brown, Anand Chandrasekaran, Paul Winget, Mathew D. Halls
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-01-01
Series:Frontiers in Chemistry
Subjects:
screening
materials
OLED
optoelectronics
machine learning
HTL
Online Access:https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/full

Internet

https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/full

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