| Title: |
Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| Authors: |
Hadi Abroshan, H. Shaun Kwak, Yuling An, Christopher Brown, Anand Chandrasekaran, Paul Winget, Mathew D. Halls |
| Source: |
Frontiers in Chemistry, Vol 9 (2022) |
| Publisher Information: |
Frontiers Media S.A., 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Chemistry |
| Subject Terms: |
screening, materials, OLED, optoelectronics, machine learning, HTL, Chemistry, QD1-999 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2296-2646 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/full; https://doaj.org/toc/2296-2646 |
| DOI: |
10.3389/fchem.2021.800371 |
| Access URL: |
https://doaj.org/article/d4c36d13675c4d7dadb86e2818ce9771 |
| Accession Number: |
edsdoj.4c36d13675c4d7dadb86e2818ce9771 |
| Database: |
Directory of Open Access Journals |
