论文标题
颗粒间相互作用与液体的物理特性之间是否存在一对一的对应关系？
Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?
论文作者
论文摘要
在这项研究中，我们介绍了从统计平均结构数据中重建颗粒间相互作用的潜力的原始方法，即许多粒子系统中颗粒的径向分布函数。该方法属于机器学习方法家族，并通过差分进化算法实现。如以Lennard-Jones液体为例的情况所证明的那样，在某个热力学状态下，许多粒子无序系统的结构与颗粒间相互作用之间没有一对一的对应关系。也就是说，由两个参数$ p_ {1} $和$ p_ {2} $确定的整个MIE电位家族，根据特定规则相互关联，可以在给定的热力学状态下正确地重现Lennard-Jones液体的独特结构。值得注意的是，该电位家族非常正确地重现了Lennard-Jones液体的运输特性（尤其是在温度范围内的自扩散系数）以及动态结构因子，这是颗粒集体动力学的关键特征之一。
In this study, we present the original method for reconstructing the potential of interparticle interaction from statistically averaged structural data, namely, the radial distribution function of particles in many-particle system. This method belongs to a family of machine learning methods and is implemented through the differential evolution algorithm. As demonstrated for the case of the Lennard-Jones liquid taken as an example, there is no one-to-one correspondence between structure and potential of interparticle interaction of a many-particle disordered system at a certain thermodynamic state. Namely, a whole family of the Mie potentials determined by two parameters $p_{ 1 }$ and $p_{ 2 }$ related to each other according to a certain rule can reproduce properly a unique structure of the Lennard-Jones liquid at a given thermodynamic state. It is noteworthy that this family of the potentials quite correctly reproduces for the Lennard-Jones liquid the transport properties (in particular, the self-diffusion coefficient) over a temperature range as well as the dynamic structure factor, which is one of the key characteristics of the collective dynamics of particles.
