Framework of an AI4M Approach for Organic Light-Emitting Diode (OLED) Material Design Covering Luminescent Materials, Quantum Chemistry, ML-Based Prediction and Interpretability, and Generative Screening Strategies. (IMAGE)
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This schematic illustrates the integrated AI for Materials (AI4M) framework proposed for OLED material design. It starts from (I) Basic Luminescent Materials, focusing on structural and photophysical properties such as IQE, PLQY, FWHM, and stability that constructing optimizing goals for material design; it then highlights three major components involved in AI4M framework: (II) Quantum Chemistry Calculations, employing DFT and advanced post-HF methods to provide reliable molecular descriptors and datasets; (III) Prediction Models and Model Interpretation, using machine learning to establish property prediction models and identify key molecular features; and (IV) Molecular Generation and Screening, applying high-throughput screening and inverse design to efficiently explore and optimize chemical space.
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