Accurate Prediction of Electric Fields of Nanoparticles With Deep Learning Methods

Abstract

Three different deep learning models were designed in this paper, to predict the electric fields of single nanoparticles, dimers, and nanoparticle arrays. For single nanoparticles, the prediction error was 4.4%. For dimers with strong couplings, a sample self-normalization method was proposed, and the error was reduced by an order of magnitude compared with traditional methods. For nanoparticle arrays, the error was reduced from 28.8% to 5.6% compared with previous work. Numerical tests proved the validity of the proposed deep learning models, which have potential applications in the design of nanostructures.