  Amorphous ice phases are key constituents of water's complex structural
landscape. This study investigates the polyamorphic nature of water, focusing
on the complexities within low-density amorphous ice (LDA), high-density
amorphous ice (HDA), and the recently discovered medium-density amorphous ice
(MDA). We use rotationally-invariant, high-dimensional order parameters to
capture a wide spectrum of local symmetries for the characterisation of local
oxygen environments. We train a neural network (NN) to classify these local
environments, and investigate the distinctiveness of MDA within the structural
landscape of amorphous ice. Our results highlight the difficulty in accurately
differentiating MDA from LDA due to structural similarities. Beyond water, our
methodology can be applied to investigate the structural properties and phases
of disordered materials.
