Climate models must accurately represent the direct radiative effects of simulated aerosol populations, but directly computing aerosol optical properties is computationally expensive. This work develops and tests two neural network emulators capable of estimating the bulk optical properties of populations of both homogeneous spherical particles and coated spheres, that can be used to replace the existing parameterization in the Energy Exascale Earth System Model’s (E3SM) Atmosphere Model (EAM). The homogeneous sphere model reproduces simulated bulk optical properties from Mie scattering code with only negligible error and provides a significant improvement in accuracy over the existing parameterization. Meanwhile, the core-shell model introduces the capacity to represent sulfate coated black carbon and moves beyond the capabilities of the existing parameterization. The neural networks leverage symmetry in the Mie calculations to provide accurate solutions for a large range of particle size and wavelength combinations and are well suited for integration into other models. We will discuss the neural network development process and share initial results from integration with E3SM.