Critical Review—Data-Driven Discovery of Novel Phosphors. (29th October 2019)

• Record Type: Journal Article
• Authors: Li, Shuxing; Xie, Rong-Jun
• Is Part Of: ECS journal of solid state science and technology. Volume 9:Number 1(2020)
• Publication Date: 2019-10-29
• Subjects: Luminescence -- high-throughput calculations -- machine learning -- phosphor materials; Solid state chemistry -- Periodicals; Electronics -- Materials -- Periodicals; Electrochemistry -- Periodicals; 541.0421
• DOI: 10.1149/2.0192001JSS
• Languages: English
• ISSNs: 2162-8777

Abstract

Abstract : Innovative phosphor materials are continuously pursued with advances in solid-state lighting and display technologies. Traditional methods of searching for new phosphors are usually based on a time-consuming and laborious trail-and-error process. Newly emerging approaches, such as high-throughput calculations and machine learning, provide great potentials to significantly accelerate the design and discovery of new materials. Here, we overview the recent development on searching for phosphor materials assisted by data-driven computations, aiming to (i) clarify the composition-structure-property relationships; (ii) construct materials descriptors by computation methods; and (iii) design novel phosphors by combining computation with experiments. Finally, future prospects for data-driven discovery of novel phosphors are highlighted.