摘要
Accurate and efficient prediction of the reflectance of particulate media is crucial for advancing optical technologies. However, traditional reflectance prediction methods often struggle to balance precision with computational efficiency, limiting material design and optimization, especially for large-scale systems. Here, we developed a novel reflectance prediction framework based on the Monte Carlo method (MCM) using a machine learning (ML) strategy. This framework addresses the challenges of low computational accuracy at high particle concentrations and inefficiency in predicting high reflectance in conventional MCMs, achieving simultaneous improvements in both accuracy and efficiency. This realization comes from the mapping of the relationship between input optical features and output reflectance in MCMs by ML and the development of a new experimentally dependent scattering correction model based on this mapping. Rigorous experimental validation and numerical simulations demonstrate the framework’s accuracy, reliability, and versatility across a variety of particulate systems. Furthermore, we applied this framework to create a high-throughput optimization algorithm tailored for radiative cooling applications, effectively guiding the optimization of representative ZrO2/PDMS films and showcasing the framework’s practical potential. Overall, our approach significantly accelerates the optimization of particulate media, paving the way for the development of innovative materials with tailored optical properties.
宋效坤
PhD
张漫遥
PhD
赵其斌
副教授
我的研究关注软功能材料中的介观结构调控及其光学、热学与力学功能。我们以胶体、颗粒组装和聚合物复合体系为主要材料平台，研究剪切、弯曲、拉伸和循环形变等外部力学场如何驱动微结构重排、结晶、晶格转变与取向选择，并进一步调控材料的结构色、光谱响应、热辐射特性和力学响应。通过将软物质物理、可规模化加工和结构—性能分析相结合，研究旨在发展可编程软光子材料与功能涂层，为自适应光学表面、光热调控、传感和机械编码材料提供新的设计思路.