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

Wenqian Chen; Peiyuan Gao; Panos Stinis

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arXiv · 2309.00767

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

Wenqian Chen·Peiyuan Gao·Panos Stinis

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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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physics.comp-ph cs.LG physics.chem-ph physics.flu-dyn

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Wenqian Chen, Peiyuan Gao, Panos Stinis. 2023-09-02. Physics-informed machine learning of the correlation functions in bulk fluids. https://arxiv.org/abs/2309.00767

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