From the abstract: Radiative particles are ubiquitous in nature and in various technologies. Calculating radiative properties from known geometry and de-signs 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. Further-more, 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.