  Inverse design of nanoparticles for desired scattering spectra and dynamic
switching between the two opposite scattering anomalies, i.e. superscattering
and invisibility, is important in realizing cloaking, sensing and functional
devices. However, traditionally the design process is quite complicated, which
involves complex structures with many choices of synthetic constituents and
dispersions. Here, we demonstrate that a well-trained deep-learning neural
network can handle these issues efficiently, which can not only forwardly
predict scattering spectra of multilayer nanoparticles with high precision, but
also inversely design the required structural and material parameters
efficiently. Moreover, we show that the neural network is capable of finding
out multi-wavelength invisibility-to-superscattering switching points at the
desired wavelengths in multilayer nanoparticles composed of metals and
phase-change materials. Our work provides a useful solution of deep learning
for inverse design of nanoparticles with dynamic scattering spectra by using
phase-change materials.
