利用深度学习势能研究 CaO3 的熔融曲线和相图。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-10-31 Epub Date: 2024-10-18 DOI:10.1021/acs.jpca.4c03074
Shuai-Jun Liu, Zhen-Shuai Lei, Meng-Ru Chen, Ting Song, Zi-Jiang Liu, Xiao-Wei Sun
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引用次数: 0

摘要

行星深层岩石部分的熔融在行星形成、地震、磁场产生、热对流和地壳演化等地质过程中发挥着重要作用。这些过程是了解行星内部动力学、行星演化历史和机制的关键途径。在此,我们通过深度学习势能模型研究了下地幔候选矿物 CaO3 的熔化曲线和压力-温度(P-T)相图。利用第一原理、分子动力学和准谐波近似,通过计算分别由空间群 Aea2 和 P4̅21m 描述的正方和四方结构 CaO3 的高温高压状态方程和相变压力,验证了深度学习势模型的可靠性。通过单相法、空隙法和两相法分别得到了零压下 975、850 和 755 K 的熔化温度,并通过径向分布函数和均方位移分析了它们的熔化过程。最后,用两相法得到了 CaO3 在 0-135 GPa 下的熔融相图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation on Melting Curves and Phase Diagrams for CaO3 Using Deep Learning Potentials.

Melting in the deep rocky parts of planets plays an important role in geological processes such as planet formation, seismicity, magnetic field generation, thermal convection, and crustal evolution. Such processes are the key way to understanding the dynamics of planetary interiors and the history as well as mechanisms of planetary evolution. We herein investigate the melting curves and pressure-temperature (P-T)-phase diagrams for CaO3, a candidate mineral for the lower mantle, by means of the deep learning potential model. Using first-principles, molecular dynamics, and quasi-harmonic approximation, the reliability of the deep learning potential model is verified by calculating the high-temperature and high-pressure equations of state and phase transition pressures for the orthorhombic and tetragonal structures of CaO3 described by space groups Aea2 and P4̅21m, respectively. The melting temperatures of 975, 850, and 755 K at zero pressure are obtained by the single-phase, void, and two-phase methods, respectively, and their melting temperatures are analyzed by the radial distribution function and mean-square displacement to analyze the melting process. Finally, the melting phase diagrams of CaO3 at 0-135 GPa were obtained by the two-phase method.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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