Research Advances in Machine Learning-driven Development of Novel Luminescent Materials

doi:10.15541/jim20250368

Abstract

Abstract: Phosphor materials are essential functional components in modern optoelectronics, playing a crucial role in next-generation displays, solid-state lighting, biomedical imaging, and sensing applications. However, the properties of materials are stringently required in multiple dimensions for practical applications. The conventional trial-and-error approach impedes the development of novel high-performance phosphors due to its long cycles and high costs. In recent years, the rapidly advancing machine learning (ML) methodology presents an innovative solution to addressing these bottlenecks. It not only dramatically accelerates the high-throughput virtual screening of candidate materials by establishing complex mapping among “composition-structure-property”, substantially improving efficiency, but also provides novel theoretical insights into structure-property correlations through algorithm-derived importance weights. This review systematically outlines the methodological framework for ML-driven phosphor development, covering key stages including data preparation, feature engineering, model selection, model evaluation and application. It further discusses specific considerations and corresponding strategies for each stage of the process. Subsequently, it reviews recent research progress in applying ML to predict key phosphor properties, encompassing critical metrics such as emission wavelength, full width at half-maximum (FWHM), thermal stability, fluorescence lifetime, centroid shift, and process optimization. Finally, this review addresses existing challenges in current research, such as scarcity of reliable data, complexity of materials properties, and difficulties in parameter quantification. The article concludes by outlining future development trends for the deep integration of artificial intelligence (AI) technology in luminescent materials research, aiming to provide a valuable reference for promoting the establishment of an “AI for Science” paradigm in the field of phosphor materials.

Key words: machine learning, phosphors, artificial intelligence, data-driven, luminescent materials, AI for science, review

CLC Number:

O482

SONG Kunjie, XIE Rongjun. Research Advances in Machine Learning-driven Development of Novel Luminescent Materials[J]. Journal of Inorganic Materials, DOI: 10.15541/jim20250368.

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