pyGDM -- A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures

TL;DR

pyGDM is a versatile Python toolkit for full-field nano-optics simulations and optimization, enabling efficient large-scale problems, 3D modeling, and physical quantity calculations using the Green Dyadic Method.

Contribution

It introduces a generalized propagator for efficient large-scale simulations and integrates evolutionary optimization for nanostructure design.

Findings

Efficiently solves large monochromatic nano-optics problems.

Supports 3D problems with substrates.

Includes tools for physical quantity calculations.

Abstract

pyGDM is a python toolkit for electro-dynamical simulations in nano-optics based on the Green Dyadic Method (GDM). In contrast to most other coupled-dipole codes, pyGDM uses a generalized propagator, which allows to cost-efficiently solve large monochromatic problems such as polarization-resolved calculations or raster-scan simulations with a focused beam or a quantum-emitter probe. A further peculiarity of this software is the possibility to very easily solve 3D problems including a dielectric or metallic substrate. Furthermore, pyGDM includes tools to easily derive several physical quantities such as far-field patterns, extinction and scattering cross-section, the electric and magnetic near-field in the vicinity of the structure, the decay rate of quantum emitters and the LDOS or the heat deposited inside a nanoparticle. Finally, pyGDM provides a toolkit for efficient evolutionary optimization of nanoparticle geometries in order to maximize (or minimize) optical properties such as a scattering at selected resonance wavelengths.