Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Amorphous phosphorus (a‐P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a‐P at the atomistic level remains a challenge. Here, it is shown that large‐scale molecular‐dynamics simulations, enabled by a machine‐learning (ML)‐based interatomic potential for phosphorus, can give new insights into the atomic structure of a‐P and how this structure changes under pressure. The structural model so obtained contains abundant five‐membered rings, as well as more complex seven‐ and eight‐atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium‐range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a‐P, and more generally it exemplifies how ML‐driven modeling can accelerate the understanding of disordered functional materials. Machine‐learning‐driven simulations provide new insight into the structure of amorphous phosphorus. The structural model so obtained contains abundant five‐membered rings and more complex cluster fragments. The simulation results help to understand previous experimental observations at ambient and high pressure, and they provide an example of the emerging application of machine‐learned potentials to complex functional materials.