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Boron phosphide (BP) is a (super)hard semiconductor constituted of lightelements, which is promising for high demand applications at extremeconditions. The behavior of BP at high temperatures and pressures is of specialinterest but is also poorly understood because both experimental andconventional ab initio methods are restricted to studying refractory covalentmaterials. The use of machine learning interatomic potentials is arevolutionary trend that gives a unique opportunity for high-temperature studyof materials with ab initio accuracy. We develop a deep machine learningpotential (DP) for accurate atomistic simulations of solid and liquid phases ofBP as well as their transformations near the melting line. Our DP providesquantitative agreement with experimental and ab initio molecular dynamics datafor structural and dynamic properties. DP-based simulations reveal that atambient pressure tetrahedrally bonded cubic BP crystal melts into an openstructure consisting of two interpenetrating sub-networks of boron andphosphorous with different structures. Structure transformations of BP meltsunder compressing are reflected by the evolution of low-pressure tetrahedralcoordination to high-pressure octahedral coordination. The main contributionsto structural changes at low pressures are made by the evolution ofmedium-range order in B-subnetwork and at high pressures by the change ofshort-range order in P-sub-network. Such transformations exhibit an anomalousbehavior of structural characteristics in the range of 12--15 GPa. Analysis ofthe results obtained raise open issues in developing machine learningpotentials for covalent materials and stimulate further experimental andtheoretical studies of melting behavior in BP.

Local structure, thermodynamics, and melting curve of boron phosphide at high pressures by deep learning-driven ab initio simulations