Unraveling Multiphase Growth Dynamics of Phosphorus on Ag(111): Machine Learning-Driven Design Principles for Controlled Synthesis

C He and L Dong and WL Yang and SG Xu and CM Zhao and LZ Tang and PY Qin and XX Dong and H Xu, ADVANCED FUNCTIONAL MATERIALS, 35 (2025).

DOI: 10.1002/adfm.202506096

Elemental phosphorus (P) nanomaterials exhibit extraordinary polymorphism that dictates their electronic and quantum properties, yet controlling phase evolution during growth remains a fundamental challenge. By integrating atomic-scale scanning tunneling microscopy (STM) with a high-precision machine learning force field (MLFF), the multiphase competition governing P growth on Ag(111) is decoded. Through cumulative molecular dynamics simulations exceeding 600 nanoseconds, three distinct regimes are revealed: 1) low-coverage chain dominance via rapid P4 decomposition (0.25 atomic layers, AL), 2) collision-driven pentamer formation at intermediate coverage (0.71 AL), and 3) high- temperature random nucleation of blue phosphorene (1.5 AL). Crucially, pentamer-mediated kinetic pathways are identified as the origin of polycrystalline blue phosphorene boundaries, explaining experimental observations of limited domain sizes. This STM-MLFF synergistic framework establishes design principles for polymorph control, demonstrated by achieving room-temperature pentamer arrays through coverage engineering. The approach provides valuable insights into the synthesis of low-dimensional phosphorus structures on metal substrate surfaces.

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