Composition-based machine learning for predicting and designing Mn 4+ -doped phosphors

RSC Advances 2026 Open access

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Authors: Ngo T. Que, Vu Dinh Huan, Le Thanh Duy, Vu N. Bao, Vu L. Minh, Mai Xuan Trang, Anh D. Phan, Phạm Thành Huy
DOI: 10.1039/d6ra00029k
Cited by: 3
References: 55

Abstract

-doped systems and achieve high predictive accuracy. An inverse design model is further developed to suggest candidate phosphor compositions for target optical outputs. By avoiding complex descriptors while preserving accuracy and interpretability, this work provides a foundation for theory-informed discovery of luminescent materials.

Fields of Study & Subjects

Luminescence Properties of Advanced Materials Inorganic Chemistry and Materials Ammonia Synthesis and Nitrogen Reduction

Publication details

Journal / source
RSC Advances
Publisher
Royal Society of Chemistry (RSC)
Volume
16
Issue
13
Pages
11415–11425
Publication date
1 January 2026
Language
EN