We employ neural networks to improve and speed up optical force calculations for dielectric particles. The network is first trained on a limited set of data obtained through accurate light scattering calculations, based on the transition matrix (T-matrix) method, and then is used to explore a wider range of particle dimensions, refractive indices, and excitation wavelengths. This computational approach is very general and flexible. Here, we focus on its application in the context of micro- and nanoplastics, a topic of growing interest in the past decade due to their widespread presence in the environment and potential impact on human health and the ecosystem
