Radiative particles are ubiquitous in nature and in various technologies. Calculating radiative properties from known geometry and designs can be computationally expensive, and trying to invert the problem to come up with designs specific to desired radiative properties is even more challenging. Here, we report a machine-learning (ML)-based method for both the forward and inverse problem for dielectric and metallic particles. Our decision-tree-based model is able to provide explicit design rules for inverse problems. Furthermore, we can use the same trained model for both the forward and the inverse problem, which greatly simplifies the computation. Our methodology shows the promise of augmenting optical design optimizations by providing interpretable and actionable design rules for rapidly finding approximate solutions for the inverse design problem.

辐射粒子在自然界和各种技术中无处不在。根据已知的几何形状和设计来计算辐射特性可能在计算上是昂贵的，并且试图将问题转化为针对所需辐射特性的特定设计甚至更具挑战性。在这里，我们报告了一种基于机器学习（ML）的方法，用于介电和金属粒子的正向和反向问题。我们基于决策树的模型能够为逆问题提供明确的设计规则。此外，对于正向和逆向问题，我们都可以使用相同的训练模型，从而大大简化了计算。