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 the 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, a 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 melt 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 the B-subnetwork and, at high pressures, by the change of short-range order in the P-subnetwork. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12–15 GPa. DP-based simulations reveal that the Tm(P) curve develops a maximum at P ≈ 13 GPa, whereas experimental studies provide two separate branches of the melting curve, which demonstrate the opposite behavior. Analysis of the results obtained raises open issues in developing machine learning potentials for covalent materials and stimulates further experimental and theoretical studies of melting behavior in BP.

中文翻译：

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通过深度学习驱动的从头计算模拟磷化硼在高压下的局部结构、热力学和熔化

磷化硼（BP）是一种由轻元素构成的（超）硬半导体，在极端条件下的高要求应用中具有广阔的前景。BP 在高温高压下的行为特别令人感兴趣，但人们对其知之甚少，因为实验和传统的从头计算方法都仅限于研究难熔共价材料。机器学习原子间势的使用是一种革命性趋势，为从头开始精确地进行材料高温研究提供了独特的机会。我们开发了深度机器学习潜力 (DP)，用于对 BP 的固相和液相及其在熔线附近的转变进行精确的原子模拟。我们的 DP 提供与结构和动态特性的实验和从头算分子动力学数据的定量一致性。基于DP的模拟表明，在环境压力下，四面体键合的立方BP晶体熔化成开放结构，该结构由具有不同结构的硼和磷的两个互穿子网络组成。BP熔体在压缩下的结构转变表现为低压四面体配位向高压八面体配位的演化。低压下结构变化的主要贡献是 B 子网中中程有序的演化，而高压下则是 P 子网中短程有序的变化。这种转变在 12-15 GPa 范围内表现出结构特征的异常行为。基于 DP 的模拟表明，Tm(P) 曲线在 P ≈ 13 GPa 时达到最大值，而实验研究提供了熔化曲线的两个独立分支，这表明了相反的行为。对所得结果的分析提出了开发共价材料机器学习潜力的开放性问题，并激发了对 BP 熔化行为的进一步实验和理论研究。