A Superior Descriptor of Random Textures and Its Predictive Capacity

TL;DR

This paper introduces the two-point cluster function $C_2({\bf r})$ as a superior descriptor for random textures, demonstrating its ability to accurately reconstruct diverse textures and predict their physical properties.

Contribution

The study identifies $C_2({\bf r})$ as a more effective two-point function for characterizing random textures, advancing the understanding of their structural information and predictive modeling.

Findings

$C_2({\bf r})$ captures topological connectedness in textures.

Accurate reconstruction of textures from materials science, cosmology, and granular media.

Suggests new pathways for predicting physical properties of random textures.

Abstract

Two-phase random textures abound in a host of contexts, porous and composite media, ecological structures, biological media and astrophysical structures. Questions surrounding the spatial structure of such textures continue to pose many theoretical challenges. For example, can two-point correlation functions be identified that can be both manageably measured and yet reflect nontrivial higher-order structural information about the textures? We present a novel solution to this question by probing the information content of the widest class of different types of two-point functions examined to date using inverse "reconstruction" techniques. This enables us to show that a superior descriptor is the two-point cluster function $C_2({\bf r})$, which is sensitive to topological {\it connectedness} information. We demonstrate the utility of $C_2({\bf r})$ by accurately reconstructing textures drawn from materials science, cosmology and granular media, among other examples. Our work suggests an entirely new theoretical pathway to predict the bulk physical properties of random textures, and also has important ramifications for atomic and molecular systems.