Zhuo Ya, Brgoch Jakoah
Department of Chemistry, University of Houston, Houston, Texas 77204, United States.
The Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States.
J Phys Chem Lett. 2021 Jan 21;12(2):764-772. doi: 10.1021/acs.jpclett.0c03203. Epub 2021 Jan 10.
Luminescent materials are continually sought for application in solid-state LED-based lighting and display applications. This has traditionally required extensive experimental effort. More recently, the employment of data-driven approaches in materials science has provided an alternative avenue to accelerate the discovery and development of luminescent materials. In this Perspective, we give an overview of luminescent materials used for lighting and display applications with a specific focus on inorganic phosphors, quantum dots, and organic light-emitting diodes. We discuss recent progress using data-driven approaches to discover new compounds, predict optical properties, and optimize synthesis, among other topics for each type of material. We then highlight future research directions focusing on using artificial intelligence (AI) to advance these fields and address some cross-cutting challenges limiting the current application of AI techniques in luminescence-related research.
人们一直在寻找发光材料,用于基于固态发光二极管的照明和显示应用。传统上,这需要大量的实验工作。最近,材料科学中数据驱动方法的应用为加速发光材料的发现和开发提供了一条替代途径。在这篇综述中,我们概述了用于照明和显示应用的发光材料,特别关注无机磷光体、量子点和有机发光二极管。我们讨论了使用数据驱动方法在发现新化合物、预测光学性质和优化合成等方面的最新进展,以及每种材料的其他相关主题。然后,我们重点介绍了未来的研究方向,即利用人工智能(AI)推动这些领域的发展,并解决一些限制AI技术在发光相关研究中当前应用的交叉挑战。