Interpretable Forward and Inverse Design of Particle Spectral Emissivity Using Common Machine-Learning Models

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. [Display omitted] Radiative particles are ubiquitous in nature, and energy and biomedical technologiesParticle spectral emissivity is modeled and investigated using simple decision treesThe same trained model can solve both the forward and inverse problemsThe highly interpretable models return explicit design rules for the inverse problem Inverse design is usually done by expensive, slow, iterative optimization. Elzouka et al. show how a simple machine-learning model (decision trees) can efficiently perform inverse design in one shot, while recovering design rules understandable by humans. Inverse design of the spectral emissivity of particles is used as an example.