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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Chemistry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/full |
| _version_ | 1819283375790227456 |
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| author | Hadi Abroshan H. Shaun Kwak Yuling An Christopher Brown Anand Chandrasekaran Paul Winget Mathew D. Halls |
| author_facet | Hadi Abroshan H. Shaun Kwak Yuling An Christopher Brown Anand Chandrasekaran Paul Winget Mathew D. Halls |
| author_sort | Hadi Abroshan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T01:30:29Z |
| format | Article |
| id | doaj.art-d4c36d13675c4d7dadb86e2818ce9771 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-24T01:30:29Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-d4c36d13675c4d7dadb86e2818ce97712022-12-21T17:22:23ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462022-01-01910.3389/fchem.2021.800371800371Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic ElectronicsHadi Abroshan0H. Shaun Kwak1Yuling An2Christopher Brown3Anand Chandrasekaran4Paul Winget5Mathew D. Halls6Schrödinger, Inc., Portland, OR, United StatesSchrödinger, Inc., Portland, OR, United StatesSchrödinger, Inc., New York, NY, United StatesSchrödinger, Inc., New York, NY, United StatesSchrödinger, Inc., New York, NY, United StatesSchrödinger, Inc., New York, NY, United StatesSchrödinger, Inc., San Diego, CA, United StatesData-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.https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/fullscreeningmaterialsOLEDoptoelectronicsmachine learningHTL |
| spellingShingle | Hadi Abroshan H. Shaun Kwak Yuling An Christopher Brown Anand Chandrasekaran Paul Winget Mathew D. Halls Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics Frontiers in Chemistry screening materials OLED optoelectronics machine learning HTL |
| title | Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| title_full | Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| title_fullStr | Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| title_full_unstemmed | Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| title_short | Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics |
| title_sort | active learning accelerates design and optimization of hole transporting materials for organic electronics |
| topic | screening materials OLED optoelectronics machine learning HTL |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2021.800371/full |
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