  Boron phosphide (BP) is a (super)hard semiconductor constituted of light
elements, which is promising for high demand applications at extreme
conditions. The behavior of BP at high temperatures and pressures is of special
interest but is also poorly understood because both experimental and
conventional ab initio methods are restricted to studying refractory covalent
materials. The use of machine learning interatomic potentials is a
revolutionary trend that gives a unique opportunity for high-temperature study
of materials with ab initio accuracy. We develop a deep machine learning
potential (DP) for accurate atomistic simulations of solid and liquid phases of
BP as well as their transformations near the melting line. Our DP provides
quantitative agreement with experimental and ab initio molecular dynamics data
for structural and dynamic properties. DP-based simulations reveal that at
ambient pressure tetrahedrally bonded cubic BP crystal melts into an open
structure consisting of two interpenetrating sub-networks of boron and
phosphorous with different structures. Structure transformations of BP melts
under compressing are reflected by the evolution of low-pressure tetrahedral
coordination to high-pressure octahedral coordination. The main contributions
to structural changes at low pressures are made by the evolution of
medium-range order in B-subnetwork and at high pressures by the change of
short-range order in P-sub-network. Such transformations exhibit an anomalous
behavior of structural characteristics in the range of 12--15 GPa. Analysis of
the results obtained raise open issues in developing machine learning
potentials for covalent materials and stimulate further experimental and
theoretical studies of melting behavior in BP.
