Generative machine learning for accelerated discovery of OLED materials

Hadi Abroshan; Shaun Kwak; Yuling An; Christopher Brown; Mathew D. Halls

doi:10.1117/12.2598141

1 August 2021 Generative machine learning for accelerated discovery of OLED materials

Hadi Abroshan, Shaun Kwak, Yuling An, Christopher Brown, Mathew D. Halls

Proceedings Volume 11808, Organic and Hybrid Light Emitting Materials and Devices XXV; 118081O (2021) https://doi.org/10.1117/12.2598141
Event: SPIE Organic Photonics + Electronics, 2021, San Diego, California, United States

Conference Poster

Abstract

Development and characterization of novel OLED materials by traditional computational approaches are challenging owing to the complex factors that simultaneously influence the device performance. In this work, we will provide an overview of generative OLED materials discovery using the latest deep neural network formalism, and show an illustrative example to design novel OLED hole-transport materials. The outcome of the work will demonstrate the value of systematic and fundamental understanding of structure-property correlations that can lead to rational design of smart OLEDs with higher efficiency.

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Hadi Abroshan, Shaun Kwak, Yuling An, Christopher Brown, and Mathew D. Halls "Generative machine learning for accelerated discovery of OLED materials", Proc. SPIE 11808, Organic and Hybrid Light Emitting Materials and Devices XXV, 118081O (1 August 2021); https://doi.org/10.1117/12.2598141

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