Jiang et al. J Mater Inf 2022;2:14 DOI: 10.20517/jmi.2022.21 Journal of Materials Informatics © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. www.jmijournal.com Open Access Review Article A mini review of machine learning in inorganic phosphors Lipeng Jiang 1 , Xue Jiang 1 , Guocai Lv 2 , Yanjing Su 1* 1 Beijing Advanced Innovation Center for Materials Genome Engineering, Corrosion and Protection Center, University of Science and Technology Beijing, Beijing 100083, China. 2 Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing 100083, China. * Correspondence to: Prof./Dr. Yanjing Su, Beijing Advanced Innovation Center for Materials Genome Engineering, Corrosion and Protection Center, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing 100083, China. E-mail: yjsu@ustb.edu.cn How to cite this article: Jiang L, Jiang X, Lv G, Su Y. A mini review of machine learning in inorganic phosphors. J Mater Inf 2022;2:14. https://dx.doi.org/10.20517/jmi.2022.21 Received: 19 Jul 2022 First Decision: 15 Aug 2022 Revised: 2 Sep 2022 Accepted: 9 Sep 2022 Published: 16 Sep 2022 Academic Editor: Xingjun Liu Copy Editor: Jia-Xin Zhang Production Editor: Jia-Xin Zhang Abstract Machine learning has promoted the rapid development of materials science. In this review, we provide an overview of recent advances in machine learning for inorganic phosphors. We take two aspects of material properties prediction and optimization based on iterative experiments as entry points to outline the applications of machine learning for inorganic phosphors in terms of Debye temperature prediction and luminescence intensity and thermal stability optimization. By analyzing the machine learning methods and their application objectives, current problems are summarized and suggestions for subsequent development are proposed. Keywords: Machine learning, phosphors, materials genome initiative INTRODUCTION Data-driven machine learning (ML) has become the frontier of materials science since the Materials Genome Initiative (MGI) for Global Competitiveness program was launched [1-4] . After several years of rapid development, ML has yielded promising achievements in novel materials development [5-7] . Hart et al. reviewed the progress of ML-assisted alloy research and summarized the typical applications of ML in amorphous, high-entropy and shape memory alloys, magnetic materials and superalloys [8] . Liu et al.