Physics-informed machine learning of the correlation functions in bulk fluids
This work addresses computational challenges in thermodynamic state theory for fluids, representing an incremental improvement with domain-specific impact.
The authors tackled solving the Ornstein-Zernike equation for pair correlation functions in bulk fluids using physics-informed machine learning models, achieving great accuracy and high efficiency in forward and inverse problems.
The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.