MD data of bulk supercritical water (Supercritical Fluids Behave as Complex Networks by Simeski and Ihme)

Supercritical fluids play a key role in environmental, geological, and cellestial processes, and are of great importance to many scientific and engineering applications due to the rapid variation of thermodynamic response functions in the supercritical phase. These properties have been hypothesized to stem from the microstructural behavior of supercritical fluids, which is characterized by density inhomogeneities. However, a direct connection between thermodynamic conditions and fluid topology, as described by molecular clusters, remains an outstanding issue. By utilizing a first-principles-based criterion and self-similarity analysis, we identify energetically localized molecular clusters whose size distribution and connectivity exhibit self-similarity in the extended supercritical phase space. We find that the structural response of these clusters is well represented by a complex network whose dynamics arise from the energetics of isotropic molecular interactions. Furthermore, we demonstrate that a hidden variable network model accurately describes the structural and dynamical response of supercritical fluids. These results highlight the need for constitutive models in supercritical fluid thermodynamics and provide a basis to relate the fluid structure to thermodynamic response functions.

超临界流体（Supercritical fluids）在环境、地质与天体过程中扮演关键角色，且由于超临界相区内热力学响应函数（thermodynamic response functions）的快速变化，其在众多科学与工程应用中具备极高的重要性。这类特性曾被假设源自超临界流体的微观结构行为，而该行为以密度不均匀性为典型特征。然而，如分子团簇（molecular clusters）所表征的热力学条件与流体拓扑结构之间的直接关联，仍是一个悬而未决的难题。我们借助基于第一性原理的判据（first-principles-based criterion）与自相似性分析（self-similarity analysis），识别出在扩展超临界相空间中呈现自相似特性的能量局域化分子团簇，其尺寸分布与连通性均符合自相似规律。我们发现，此类团簇的结构响应可通过复杂网络（complex network）得到精准表征，而该网络的动力学特性源于各向同性分子相互作用的能量学机制。此外，我们证实隐变量网络模型（hidden variable network model）可准确描述超临界流体的结构与动力学响应。上述研究结果凸显了超临界流体热力学本构模型（constitutive models）构建的必要性，并为建立流体结构与热力学响应函数之间的关联提供了坚实的理论基础。