Faster calculations of optical trapping using neural networks trained by T-matrix data: an application to micro and nanoplastics

TL;DR

This paper introduces a neural network approach trained on T-matrix data to rapidly compute optical forces on dielectric particles, aiding research on micro and nanoplastics with improved efficiency.

Contribution

The study presents a novel neural network method trained on T-matrix data for fast optical force calculations, enhancing environmental research on micro and nanoplastics.

Findings

Neural network accurately predicts optical forces across various particle parameters.

Significant speed-up in optical force calculations compared to traditional methods.

Application demonstrated in micro and nanoplastics research.

Abstract

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 method, and then 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 last decade due to their widespread presence in the environment and potential impact on human health and the ecosystem.