How machine learning approaches are useful in computing the optical properties of non-spherical particles across a broad range of size parameters?

• Machine learning excelled at extrapolating optical properties of non-spherical particles with larger size parameters. • Deep Neural Networks (DNN) outperformed the improved geometric optics method for particles with moderate size parameters, accounting for edge effects. • DNN efficiently computed optical properties of super-spheroids across a wide range of size parameters. This study investigates the use of machine learning techniques, specifically deep neural networks (DNN), to compute the optical properties of non-spherical particles across a wide range of size parameters. The approach involves training a DNN using a T-matrix database of super-spheroids with size parameters below 50. The DNN is then able to predict optical properties for size parameters beyond this limit, with exceptional accuracy observed for size parameters between 50–100. These predictions outperform the improved geometric optics method (IGOM). To further enhance accuracy and broaden the range of size parameters, databases are merged, combining the T-matrix results for small (0.1–50) and some moderate size parameters (50–100), the DNN predicted values for moderate size parameters (50–100), and the IGOM results for large size parameters (>100). This merged database is used to train a new DNN. This comprehensive training enables the neural networks to calculate the optical properties for super-spheroids across the entire size parameter spectrum, considering various shape parameters and refractive indices. This technique effectively bridges the gap between full-electromagnetic wave results and geometric-optics approximations, providing an efficient method for computing the optical properties of non-spherical particles from Rayleigh to geometric optics domains. Notably, the DNNs automatically consider edge effects, eliminating the need for deriving complex analytical formulas for non-spherical particles. [ABSTRACT FROM AUTHOR]