计算与数据驱动环保型发光材料的研究进展OA

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.