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Thermodynamic and structural properties of the high density Gaussian core model

机译:高密度高斯核模型的热力学和结构性质

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摘要

We numerically study thermodynamic and structural properties of the one-component Gaussian core model at very high densities. The solid-fluid phase boundary is carefully determined. We find that the density dependence of both the freezing and melting temperatures obey the asymptotic relation, log Tf, log Tm∝ − ρ2/3, where ρ is the number density, which is consistent with Stillingeru27s conjecture. Thermodynamic quantities such as the energy and pressure and the structural functions such as the static structure factor are also investigated in the fluid phase for a wide range of temperature above the phase boundary. We compare the numerical results with the prediction of the liquid theory with the random phase approximation (RPA). At high temperatures, the results are in almost perfect agreement with RPA for a wide range of density, as it has already been shown in the previous studies. In the low temperature regime close to the phase boundary line, although RPA fails to describe the structure factors and the radial distribution functions at the length scales of the interparticle distance, it successfully predicts their behaviors at longer length scales. RPA also predicts thermodynamic quantities such as the energy, pressure, and the temperature at which the thermal expansion coefficient becomes negative, almost perfectly. Striking ability of RPA to predict thermodynamic quantities even at high densities and low temperatures is understood in terms of the decoupling of the length scales which dictate thermodynamic quantities from the interparticle distance which dominates the peak structures of the static structure factor due to the softness of the Gaussian core potential.
机译:我们数值研究了高密度单组分高斯核模型的热力学和结构性质。固相相界是仔细确定的。我们发现,冻结温度和熔化温度的密度依赖性都遵循渐近关系,log Tf,log Tm∝-ρ2/ 3,其中ρ是数密度,与Stillinger的猜想一致。对于相界以上的宽温度范围,还研究了在流体相中的热力学量(例如能量和压力)和结构功能(例如静态结构因子)。我们将数值结果与采用随机相位近似(RPA)的液体理论的预测进行比较。在以前的研究中已经表明,在高温下,该结果与RPA的密度范围几乎完全吻合。在接近相界线的低温状态下,尽管RPA无法描述颗粒间距离长度尺度上的结构因子和径向分布函数,但它成功地预测了它们在较长长度尺度上的行为。 RPA还可以预测热力学系数,例如能量,压力和温度,热膨胀系数几乎为负值。 RPA即使在高密度和低温下也能预测热力学量的惊人能力是通过长度尺度的解耦来理解的,该尺度决定了颗粒间距离的热力学量,而颗粒间的距离主导了静态结构因子的峰结构,这是由于其柔软性所致。高斯核心潜力。

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