Designer pair statistics of disordered many-particle systems with novel properties

Author:

Wang Haina1ORCID,Torquato Salvatore23ORCID

Affiliation:

1. Department of Chemistry, Princeton University 1 , Princeton, New Jersey 08544, USA

2. Department of Chemistry, Department of Physics, Princeton Materials Institute, and Program in Applied and Computational Mathematics, Princeton University 2 , Princeton, New Jersey 08544, USA

3. School of Natural Sciences, Institute for Advanced Study 3 , 1 Einstein Drive, Princeton, New Jersey 08540, USA

Abstract

The knowledge of exact analytical functional forms for the pair correlation function g2(r) and its corresponding structure factor S(k) of disordered many-particle systems is limited. For fundamental and practical reasons, it is highly desirable to add to the existing database of analytical functional forms for such pair statistics. Here, we design a plethora of such pair functions in direct and Fourier spaces across the first three Euclidean space dimensions that are realizable by diverse many-particle systems with varying degrees of correlated disorder across length scales, spanning a wide spectrum of hyperuniform, typical nonhyperuniform, and antihyperuniform ones. This is accomplished by utilizing an efficient inverse algorithm that determines equilibrium states with up to pair interactions at positive temperatures that precisely match targeted forms for both g2(r) and S(k). Among other results, we realize an example with the strongest hyperuniform property among known positive-temperature equilibrium states, critical-point systems (implying unusual 1D systems with phase transitions) that are not in the Ising universality class, systems that attain self-similar pair statistics under Fourier transformation, and an experimentally feasible polymer model. We show that our pair functions enable one to achieve many-particle systems with a wide range of translational order and self-diffusion coefficients D, which are inversely related to one another. One can design other realizable pair statistics via linear combinations of our functions or by applying our inverse procedure to other desirable functional forms. Our approach facilitates the inverse design of materials with desirable physical and chemical properties by tuning their pair statistics.

Funder

National Science Foundation

Army Research Office

Publisher

AIP Publishing

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