A Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem f...

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Veröffentlicht in:	arXiv.org 2023-11
Hauptverfasser:	Nadkarni, Ishan, Wu, Haiyi, Aluru, Narayana R
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Granulation Inhomogeneity Iterative methods Liquids Machine learning Molecular structure Parameter identification Physical properties Physics - Statistical Mechanics
Online-Zugang:	Volltext
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