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arXiv 1909.00231physics.comp-phcond-mat.dis-nncond-mat.other
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基于电子温度相关深势分子动力学的热稠密物质模拟

Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Yuzhi Zhang, Chang Gao, Linfeng Zhang, Han Wang, Mohan Chen

摘要:

模拟温暖的致密物质,经历了广泛的温度和密度是具有挑战性的。预测理论模型,如基于量子力学的第一性原理分子动力学(FPMD),需要大量的计算资源。在此,我们提出了一种基于深度学习的方案,称为电子温度相关的深势分子动力学(TDDPMD),用于有效地模拟温暖的致密物质与FPMD的准确性。TDDPMD模拟比FPMD快几个数量级,并且与FPMD不同,其效率不受电子温度的影响。我们应用TDDPMD计划铍(Be)在很宽的温度范围(0.4至2500 eV)和密度(3.50至8.25 g/cm $^3$)。我们的研究结果表明,TDDPMD方法不仅准确地再现了沿主Hugoniot曲线在FPMD水平的Be的结构特性,但也产生更可靠的扩散系数比典型的FPMD模拟,由于其能够模拟更大的系统与更长的时间。

英文摘要:

Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principles molecular dynamics (FPMD), require a huge amount of computational resources. Herein, we propose a deep learning based scheme, called electron temperature dependent deep potential molecular dynamics (TDDPMD), for efficiently simulating warm dense matter with the accuracy of FPMD. The TDDPMD simulation is several orders of magnitudes faster than FPMD, and, unlike FPMD, its efficiency is not affected by the electron temperature. We apply the TDDPMD scheme to beryllium (Be) in a wide range of temperatures (0.4 to 2500 eV) and densities (3.50 to 8.25 g/cm$^3$). Our results demonstrate that the TDDPMD method not only accurately reproduces the structural properties of Be along the principal Hugoniot curve at the FPMD level, but also yields even more reliable diffusion coefficients than typical FPMD simulations due to its ability to simulate larger systems with longer time.

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