Harnessing generative AI for efficient organic materials discovery in low-data regimes

Jun Hyeong Kim; Jinhee Lee; Hyeonsu Kim; MinSoo Kang; Suk-Ku Chang; Yinglan Jin; Dongwook Kim; Woo Youn Kim

doi:10.1039/d5dd00463b

January 1, 2026Digital DiscoveryOpen Access

Harnessing generative AI for efficient organic materials discovery in low-data regimes

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Key Points

The aim is to utilize generative AI for discovering organic materials, specifically TADF materials, with limited data.
Utilized a building block-based generative AI approach
Combined with DFT (Density Functional Theory) screening
Validated findings through experimental testing
Successfully designed green emitters
Achieved external quantum efficiencies of up to 11.22%

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Implication

Generative AI uncovers efficient TADF materials, indicating potential for breakthroughs in organic electronics.

Abstract

A building block-based generative AI combined with DFT screening enables the efficient discovery of TADF materials in low-data regimes. Experimental validation confirms the design of green emitters with external quantum efficiencies up to 11.22%.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69a7cd1dd48f933b5eed92a9 https://doi.org/10.1039/d5dd00463b

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Harnessing generative AI for efficient organic materials discovery in low-data regimes

Key Points

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Abstract

Discussion

Cite This Study

Harnessing generative AI for efficient organic materials discovery in low-data regimes

Key Points

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Abstract

Discussion

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