Developing organic light-emitting diodes (OLEDs) with a desired emission color and efficiency involves complex efforts in material selection and optimizing the device structure due to their multilayered architectures. Notably, the cavity structure in the OLEDs allows for a wide range of emission colors and efficiencies based on the thicknesses and optical constants of the layers, even within a specific material set. Conventional approaches to achieving optimized OLED designs can prove to be financial-, labor-, and time-intensive for researchers, considering the multitude of combinations necessary for the complex, multilayered structure. To address these challenges, this study introduces a novel machine learning (ML) algorithm capable of intelligently predicting the ideal device structure for OLEDs, considering organic layer thicknesses and refractive indexes. The rule-based ML algorithm exhibits impressive accuracy, with an error margin of less than 0.5% for red-, green-, and blue-emitting OLEDs. These findings emphasize the potential of the ML algorithm as an invaluable solution to streamline the process of obtaining optimized OLED designs, offering substantial time and resource savings with high precision.

中文翻译：

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基于人工智能的有机发光二极管精确结构建模的机器学习算法

由于其多层架构，开发具有所需发射颜色和效率的有机发光二极管 (OLED) 需要在材料选择和优化器件结构方面进行复杂的工作。值得注意的是，即使在特定的材料组内，OLED 中的腔结构也可以根据层的厚度和光学常数实现各种发射颜色和效率。考虑到复杂的多层结构所需的多种组合，对于研究人员来说，实现优化 OLED 设计的传统方法可能会耗费大量的资金、劳动力和时间。为了应对这些挑战，本研究引入了一种新颖的机器学习 (ML) 算法，能够在考虑有机层厚度和折射率的情况下智能预测 OLED 的理想器件结构。基于规则的机器学习算法表现出令人印象深刻的准确性，对于红光、绿光和蓝光 OLED 的误差幅度小于 0.5%。这些发现强调了机器学习算法作为一种宝贵解决方案的潜力，可以简化获得优化 OLED 设计的过程，以高精度节省大量时间和资源。