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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Detalles Bibliográficos
Autores principales: Abroshan, Hadi, Kwak, H. Shaun, An, Yuling, Brown, Christopher, Chandrasekaran, Anand, Winget, Paul, Halls, Mathew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802167/
https://www.ncbi.nlm.nih.gov/pubmed/35111731
http://dx.doi.org/10.3389/fchem.2021.800371

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802167/
https://www.ncbi.nlm.nih.gov/pubmed/35111731
http://dx.doi.org/10.3389/fchem.2021.800371

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