慢速淬火、高压下达到DFT精度的玻璃态B$_2$O$_3$
Slowly Quenched, High Pressure Glassy B$_2$O$_3$ at DFT Accuracy
AI总结：
本文开发了B$_2$O$_3$玻璃的机器学习势，结合深度势分子动力学模拟，在DFT精度下实现了慢速淬火和高压条件下的准确结构预测，并与实验数据高度吻合。
摘要：
无机玻璃的建模需要精确表示原子间相互作用、足够大的体系尺寸以呈现中程结构有序，以及足够慢的淬火速率以消除动力学捕获的结构基元。第一性原理分子动力学和基于力场的分子动力学模拟均无法同时满足这三项要求。本文报道了针对经典玻璃B$_2$O$_3$开发的机器学习势（MLP），该势能较好地实现了上述目标。该MLP在凝聚相构型上训练，其原子能量和力由周期性量子密度泛函理论计算获得。基于该MLP的深度势分子动力学（DPMD）模拟准确预测了状态方程以及熔体慢速淬火过程中玻璃的致密化行为。在常压条件下，淬火速率大于10$^{11}$ K/s会导致结构出现伪影。模拟得到的随压力变化的X射线和中子结构因子与实验数据高度吻合。玻璃的高压模拟显示出硼和氧的多种配位几何构型，与实验观测结果一致。
英文摘要：
Modeling inorganic glasses requires an accurate representation of interatomic interactions, large system sizes to allow for intermediate-range structural order, and slow quenching rates to eliminate kinetically trapped structural motifs. Neither first principles- nor force field-based molecular dynamics (MD) simulations satisfy these three criteria unequivocally. Herein, we report the development of a machine learning potential (MLP) for a classic glass, B$_2$O$_3$, which meets these goals well. The MLP is trained on condensed phase configurations whose energies and forces on the atoms are obtained using periodic quantum density functional theory. Deep potential MD (DPMD) simulations based on this MLP accurately predict the equation of state and the densification of the glass with slower quenching from the melt. At ambient conditions, quenching rates larger than 10$^{11}$ K/s are shown to lead to artifacts in the structure. Pressure-dependent X-ray and neutron structure factors from the simulations compare excellently with experimental data. High-pressure simulations of the glass show varied coordination geometries of boron and oxygen, which concur with experimental observations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
