计算与数据驱动环保型发光材料的研究进展

doi:10.15541/jim20250425

无机材料学报 ›› 2026, Vol. 41 ›› Issue (6): 704-722.DOI: 10.15541/jim20250425

计算与数据驱动环保型发光材料的研究进展

胡扬(), 谢敏, 张筱怡, 李想, 郭新伟, 姜南, 周文瀚, 张胜利(), 曾海波()

南京理工大学材料科学与工程学院, 南京 210094

收稿日期:2025-10-27 修回日期:2026-01-07 出版日期:2026-06-20 网络出版日期:2026-01-22
通讯作者: 张胜利, 教授. E-mail: zhangslvip@njust.edu.cn;
曾海波, 教授. E-mail: zeng.haibo@njust.edu.cn
作者简介:胡扬(1996-), 女, 博士. E-mail: hudfyang@njust.edu.cn
基金资助:
国家重点研发计划(2024YFA1210002);国家自然科学基金(52473236);国家自然科学基金(62304109)

Research Progress on Computational and Data-driven Environmental-friendly Luminescent Materials

HU Yang(), XIE Min, ZHANG Xiaoyi, LI Xiang, GUO Xinwei, JIANG Nan, ZHOU Wenhan, ZHANG Shengli(), ZENG Haibo()

School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2025-10-27 Revised:2026-01-07 Published:2026-06-20 Online:2026-01-22
Contact: ZHANG Shengli, professor. E-mail: zhangslvip@njust.edu.cn;
ZENG Haibo, professor. E-mail: zeng.haibo@njust.edu.cn
About author:HU Yang (1996-), female, PhD. E-mail: hudfyang@njust.edu.cn
Supported by:
National Key Research and Development Program of China(2024YFA1210002);National Natural Science Foundation of China(52473236);National Natural Science Foundation of China(62304109)

摘要/Abstract

摘要：

传统发光材料(如镉系量子点、铅卤化物钙钛矿)因含Cd、Pb等重金属元素, 在其全生命周期存在显著的环境与健康风险。因此, 开发无镉量子点、无铅卤化物钙钛矿、稀土掺杂荧光粉等环保型发光材料成为核心科研方向。然而, 当前环保型发光材料的研发仍高度依赖“试错式”实验模式, 不仅效率低, 也难以突破发光效率、环境稳定性与界面相容性的核心瓶颈。本文系统梳理了环保型发光材料的研究现状与现存挑战, 并阐明了密度泛函理论等计算技术可精准预测量子点核壳结构光电特性、解析缺陷致非辐射复合机制等, 从而定向预测并优化材料的发光效率与稳定性。数据驱动技术可通过构建标准化材料数据库与机器学习模型, 进一步加速材料筛选与设计, 已成功指导开发出高稳定性荧光粉、高效率窄带发射材料等。展望未来, 计算与数据驱动技术协同可破解环保型发光材料的研发困境。通过进一步推动两类技术的协同和融合, 有望加速环保型发光材料在显示、照明等领域的实际应用, 助力光电产业绿色转型。

关键词: 环保型发光材料, 密度泛函理论, 数据驱动技术, 机器学习, 钙钛矿材料, 综述

Abstract:

The development of traditional luminescent materials (such as cadmium-based quantum dots and lead halide perovskites) is intrinsically limited by their reliance on toxic heavy metals (e.g., Cd and Pb), which raises severe environmental and health risks throughout their lifecycles. Therefore, the transition toward eco-friendly alternatives, including cadmium-free quantum dots, lead-free halide perovskites, and rare-earth-doped phosphors, has become a pivotal research imperative. Currently, the design and optimization of such materials rely on inefficient trial-and-error experimental paradigms, which often fail to overcome critical bottlenecks in luminous efficiency, environmental stability, and interfacial compatibility. This review systematically outlines the current landscape and technical challenges of environmental-friendly luminescent materials. It highlights how computational techniques, particularly density functional theory, allow the accurate prediction of optoelectronic properties in core-shell structures and the elucidation of defect-induced non-radiative recombination mechanisms, thus facilitating rational material design and property optimization. In addition to theoretical calculations, data-driven technologies further accelerate material screening by leveraging standardized databases and machine learning models, having already yielded high-stability phosphors and high-efficiency narrowband emitters. Finally, an outlook on the synergy between computational and data-driven approaches to overcome existing research and development barriers is provided. Future efforts must focus on deepening the integration of these technologies to advance the practical deployment of environmental-friendly luminescent materials in display and lighting applications, thereby driving the sustainable transformation of the optoelectronics industry.

Key words: environmental-friendly luminescent material, density functional theory, data-driven technology, machine learning, perovskite material, review

中图分类号:

TB39
TN204

胡扬, 谢敏, 张筱怡, 李想, 郭新伟, 姜南, 周文瀚, 张胜利, 曾海波. 计算与数据驱动环保型发光材料的研究进展[J]. 无机材料学报, 2026, 41(6): 704-722.

HU Yang, XIE Min, ZHANG Xiaoyi, LI Xiang, GUO Xinwei, JIANG Nan, ZHOU Wenhan, ZHANG Shengli, ZENG Haibo. Research Progress on Computational and Data-driven Environmental-friendly Luminescent Materials[J]. Journal of Inorganic Materials, 2026, 41(6): 704-722.

图/表 11

图1 环保型发光材料概述

Fig. 1 Overview of environmental-friendly luminescent materials QD: quantum dot

图2 核壳量子点的能带对齐类型及壳层厚度依赖的激子分布特性[3,73 -74]

Fig. 2 Band alignment types and shell-thickness-dependent exciton distribution characteristics in core/shell quantum dots[3,73 -74] (a) Type I, type II and reverse type I band alignment (CBE: conduction band edge, VBE: valence band edge, Egc: energy band gap of core, Egs: energy band gap of shell)[3]; (b) Schematic diagram of the wave function tunneling probability estimation method[73]; (c) Electron-hole radial distribution function (RDF) of 1.75 nm CdSe core with different shell thicknesses; (d) Single exciton probability distribution vs. shell thickness (with core/shell exciton coverage)[74]

图3 InP量子点的表面化学效应、光学性质及Ga2O3晶体结构特性[34,76 -78]

Fig. 3 Surface chemistry effects and optical properties of InP QDs and crystal structure characteristics of Ga2O3[34,76 -78] (a) Influence of different surface chemistries on carrier trapping in InP QDs[76]; (b) Absorbance and emission spectra of QDs with different compositions[77]; (c) Crystal structure of Ga2O3[34]; (d) Refined lattice parameters[78]; (e) BVS[78]

图4 几种氮化物荧光粉的晶体结构及发光性能[34,80 -82]

Fig. 4 Crystal structures and luminescence properties of several nitride phosphors[34,80 -82] (a) Excitation/emission spectra of SLA and CaAlSiN3:Eu2+[80]; (b) Excitation, reflectance and emission spectra of M[Mg2Al2N4]:Eu2+ (M=Ca, Sr, Ba)[81]; (c) Excitation/emission spectra of Sr[Mg3SiN4]:Eu2+[82]; (d-f) Crystal structures of (d) SrLiAl3N4, (e) SrMg2Al2N4, and (f) SrMg3SiN4[34]

图5 InP量子点的电子结构计算及二维钙钛矿的结构畸变与电子定位分析[83-84]

Fig. 5 Electronic structure calculations of InP QDs and structural distortion with electron localization analysis of 2D perovskites[83-84] (a) Molecular orbital energy and electron density of InP QDs in the screened configuration interaction singles (SCIS) calculations[83]; (b) Schematic of octahedral coordinate system and off-centering vector[84]; (c-e) Electron localization function contour plots under different stretchings[84]

图6 Sr2.945Al0.025Si0.975O5:0.03Eu2+的晶体结构特征及La1-xSr2+xAl1-xSixO5:Eu荧光粉的能量转移机制[88]

Fig. 6 Crystal structural characteristics of Sr2.945Al0.025Si0.975O5:0.03Eu2+ and energy transfer mechanism of La1-xSr2+xAl1-xSixO5:Eu phosphors[88] (a-c) Crystal structure of Sr2.945Al0.025Si0.975O5:0.03Eu2+ (different views); (d) Corresponding coordination polyhedra; (e) Schematic of Eu ion transition and energy transfer in La1-xSr2+xAl1-xSixO5:Eu

图7 机器学习在带隙预测、Ce3+掺杂晶体光学性能及碳点发光性能预测中的应用[107,115,117]

Fig. 7 Machine learning applications in band gap prediction, optical properties of Ce3+-doped crystals, and luminescence performance prediction of carbon dots[107,115,117] (a) Band gap prediction comparison of different machine learning models[107]; (b) 4f-5d energy level diagram of Ce3+ in crystals[115]; (c) Correlation between predicted and experimental Ce3+ 4f-5d transition energy[115]; (d-f) Mean absolute error (MAE) optimization of emission wavelength, quantum yield (QY) and Stokes shift by different models[117]

图8 机器学习在Eu2+激活荧光粉发射波长预测及钙钛矿纳米晶自适应合成优化中的应用[118-119]

Fig. 8 Machine learning for emission wavelength prediction of Eu2+-activated phosphors and adaptive synthesis optimization of perovskite nanocrystals[118-119] (a) Training, validation and test results of peak emission wavelength (PEW) prediction[118]; (b) Synthesis setup and adaptive sampling of (Cs/FA)Pb(I/Br)3 NCs[119]; (c, d) Full width at half maximum (FWHM) and maximum intensity prediction under 680 nm emission[119]

图9 新型发光材料的相图分析、晶体结构表征及计算筛选策略[125-127]

Fig. 9 Phase diagram analysis, crystal structure characterization, and computational screening strategies for novel luminescent materials[125-127] (a) 3D phase diagram of SrO-SiO2-Si3N4-Al2O3 (with new phase Sr2AlSi2O6N)[125]; (b) Computational screening workflow of deep-ultraviolet light emitters[126]; (c) Crystal structure of (C10H16N)2SbCl5[127]; (d) Schematic of random forest algorithm[127]; (e) Photoluminescence excitation (PLE) and PL spectrum of (C10H16N)2SbCl5[127]

图10 数据驱动在Eu2+及Mn4+掺杂荧光粉结构筛选与性能分析中的应用[129,131,134,137]

Fig. 10 Data-driven structural screening and property analysis of Eu2+-doped and Mn4+-doped phosphors[129,131,134,137] (a) 2D t-SNE plots of K-centered local structures[129]; (b) Lattice structure of the candidate CNAF[131]; (c) ESL vs. 4A2→4T2 transition energy of Mn4+-doped fluorides[134]; (d) Energy level diagram of Mn4+-activated systems (long/short ESL)[134]; (e) 3D feature space distribution of high-lifetime samples[134]; (f) Schematic of ML-based screening for narrow-band green emission candidates[137]

图11 机器学习模型在发光材料性能预测及LED器件优化中的应用评估[138,141 -142]

Fig. 11 Evaluation of machine learning models in property prediction of luminescent materials and LED device optimization[138,141 -142] (a) Data processing pipeline of ML model[138]; (b) Receiver operating characteristic (ROC) curve and area under the curve (AUC) of logistic regression classifier[138]; (c) Confusion matrix of the model on cross-validation[138]; (d) Comparison of predicted and experimental CCT[141]; (e) Structure and luminescence photo of white phosphor-converted light-emitting diode (pc-LED)[142]

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计算与数据驱动环保型发光材料的研究进展

Research Progress on Computational and Data-driven Environmental-friendly Luminescent Materials

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