Physics-informed machine learning of the correlation functions in bulk fluids
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 16 of its 76 references that are in the map. First posted on arXiv 2 Sep 2023.
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