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

• URL: http://arxiv.org/abs/2309.00767v1
• Date: Sat, 2 Sep 2023 00:11:48 GMT
• Title: Physics-informed machine learning of the correlation functions in bulk fluids
• Authors: Wenqian Chen, Peiyuan Gao, Panos Stinis
• Abstract summary: 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.
• Score: 2.1255150235172837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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