水中热致电场的非平衡模拟
Non-equilibrium simulations of thermally induced electric fields in water
摘要：
使用非平衡分子动力学模拟，最近已经证明，水分子响应于施加的温度梯度而排列，从而产生有效的电场。在这里，我们研究如何热诱导场依赖于长程相互作用的基本治疗。对于短程Wolf方法和Ewald求和，我们发现对于$5.2~\text{K/}\unicode{x212B}$的温度梯度，场的峰值强度在$2 \times 10^7$和$5 \times 10^7~\text{V/m}$之间。因此，我们的Wolf方法值比文献值低一个数量级[J. Chem. Phys. 139，014504（2013）和143，036101（2015）]。我们发现，这种差异可以追溯到使用一个不正确的内核在计算的静电场。更严重的是，我们发现，沃尔夫方法无法预测正确的分子取向，导致偶极子密度与使用埃瓦尔德求和计算的符号相反。通过考虑两种不同的多极展开，我们表明，对于非均匀极化，四极的贡献可以是显着的，甚至超过了偶极子的贡献。最后，我们提出了一种更精确的计算静电势和静电场的方法。特别是，我们表明，平均微观场分析，以获得宏观麦克斯韦场减少了误差条高达一个数量级。因此，达到给定的统计精度所需的模拟时间减少了两个数量级。
英文摘要：
Using non-equilibrium molecular dynamics simulations, it has been recently demonstrated that water molecules align in response to an imposed temperature gradient, resulting in an effective electric field. Here, we investigate how thermally induced fields depend on the underlying treatment of long-ranged interactions. For the short-ranged Wolf method and Ewald summation, we find the peak strength of the field to range between $2 \times 10^7$ and $5 \times 10^7~\text{V/m}$ for a temperature gradient of $5.2~\text{K/}\unicode{x212B}$. Our value for the Wolf method is therefore an order of magnitude lower than the literature value [J. Chem. Phys. 139, 014504 (2013) and 143, 036101 (2015)]. We show that this discrepancy can be traced back to the use of an incorrect kernel in the calculation of the electrostatic field. More seriously, we find that the Wolf method fails to predict correct molecular orientations, resulting in dipole densities with opposite sign to those computed using Ewald summation. By considering two different multipole expansions, we show that, for inhomogeneous polarisations, the quadrupole contribution can be significant and even outweigh the dipole contribution to the field. Finally, we propose a more accurate way of calculating the electrostatic potential and the field. In particular, we show that averaging the microscopic field analytically to obtain the macroscopic Maxwell field reduces the error bars by up to an order of magnitude. As a consequence, the simulation times required to reach a given statistical accuracy decrease by up to two orders of magnitude.
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