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无机材料学报

无机材料学报

• 研究论文 • 上一篇    下一篇

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   

  1. 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
  • 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(1996.10-),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(52473236,62304109)

Research Progress on Computational and Data-Driven Environmentally Friendly Luminescent Materials

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

  1. School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2025-10-27 Revised:2026-01-07
  • Contact: ZHANG Shengli, professor. E-mail: zhangslvip@njust.edu.cn;ZENG Haibo, professor. E-mail: zeng.haibo@njust.edu.cn
  • About author:HU Yang, PhD. E-mail: hudfyang@njust.edu.cn
  • Supported by:
    National Key Research and Development Program of China (2024YFA1210002); National Natural Science Foundation of China (52473236,62304109)

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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。本综述系统梳理环保型发光材料的研究现状与现存挑战,并阐明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等问题。与此同时,数据驱动技术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、高效率窄带发射等材料。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, are 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 often 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 eco-friendly luminescent materials. We highlight how computational techniques, particularly density functional theory (DFT), 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, we provide an outlook on the synergy between computational and data-driven approaches to overcome existing research and development barriers. Future efforts must focus on deepening the integration of these technologies to advance the practical deployment of eco-friendly luminescent materials in display and lighting applications, thereby driving the sustainable transformation of the optoelectronics industry.

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

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