“…[18,49] Another feature that distinguishes phosphorene from graphene is the presence of a plethora of structural phases, which are absent in sp 2 -hybridized systems. Recently, a variety of stable geometrical configurations of phosphorene with nonplanar structure have been predicted by simulations (Figure 1), such as 𝛽-phosphorene, 𝛾-phosphorene, 𝛿-phosphorene, Green phosphorene (𝜆-P), 𝜖-phosphorene, 𝜁-phosphorene, 𝜂-phosphorene, 𝜃-phosphorene, 𝛿-phosphorene, 𝜓-phosphorene, mixed phosphorene allotropes (𝛼𝛽-P, 𝛽𝛾-P, 𝛾𝛿-P, 𝛼𝛾-P, 𝛼𝛿-P, 𝛽𝛿-P, 𝛼𝜖-P, 𝛽𝜖-P, 𝛾𝜖-P, 𝜁𝜖-P1, 𝜁𝜖-P2), 𝛼-P 6 , 𝛽-P 6 , red phosphorene, Kagome phosphorene, Haeckelite phosphorene, Hex-star phosphorene nanoribbon, Crimson phosphorus, square-octagon phosphorene, P 567 monolayer, and 2D porous phosphorus, [47,[50][51][52][53][54][55][56][57][58][59][60][61][62][63] of which the puckered black phosphorene allotrope (𝛼-phase) is the most stable. [50,58] Deringer et al developed a generalized machine-learning method for atomistic simulations of bulk and nanostructured forms of phosphorus, which had predicted a variety of nanowires and 2D phosphorus allotropes, and also demonstrated proof-of-concept simulations of phosphorene nanoribbons with >80-nm-long, as well as for liquid phases, breaking through the limitations of the density functional theory (DFT) code in phosphorene systems.…”