Physics-informed machine learning of the correlation functions in bulk fluids

TL;DR

This paper introduces physics-informed neural networks to accurately and efficiently solve the Ornstein-Zernike equation for pair correlation functions in bulk fluids, advancing computational methods in liquid state theory.

Contribution

It applies physics-informed neural networks and neural operator networks to solve the OZ equation, demonstrating high accuracy and efficiency in modeling bulk fluid correlations.

Findings

High accuracy in solving OZ equations

Efficient computation for forward and inverse problems

Potential for thermodynamic applications

Abstract

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.