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Predicting cluster size in particle systems

Published on:

12 March 2024

Primary Category:

Soft Condensed Matter

Paper Authors:

Florian Sammüller,

Silas Robitschko,

Sophie Hermann,

Matthias Schmidt


Key Details

Extends density functional theory to arbitrary observables

Yields associated local fluctuation profiles

Uses neural networks as functionals trained on simulations

Demonstrated for cluster statistics of model fluids

Provides access to complex order parameters

AI generated summary

Predicting cluster size in particle systems

The paper develops a method called hyper-density functional theory to predict the statistics of complex structural properties like cluster size in particle systems. It extends standard density functional theory by incorporating additional observables beyond just the density profile. Neural networks are trained on simulation data to act as functionals that provide accurate predictions. Demonstrated for cluster properties of simple model systems.

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