Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?

Anatolii V. Mokshin and Roman A. Khabibullin

Physica A: Statistical Mechanics and its Applications, 2022, vol. 608, issue P1

Abstract: In this study, we present the original method for reconstructing the potential of interparticle interaction from statistically averaged structural data, namely, the radial distribution function of particles in many-particle system. This method belongs to a family of machine learning methods and is implemented through the differential evolution algorithm. As demonstrated for the case of the Lennard-Jones liquid taken as an example, there is no one-to-one correspondence between structure and potential of interparticle interaction of a many-particle disordered system at a certain thermodynamic state. Namely, a whole family of the Mie potentials determined by two parameters p1 and p2 related to each other according to a certain rule can reproduce properly a unique structure of the Lennard-Jones liquid at a given thermodynamic state. It is noteworthy that this family of the potentials quite correctly reproduces for the Lennard-Jones liquid the transport properties (in particular, the self-diffusion coefficient) over a temperature range as well as the dynamic structure factor, which is one of the key characteristics of the collective dynamics of particles.

Keywords: Interaction potentials; Machine learning; Genetic algorithms; Liquids; Soft systems; Structure (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:608:y:2022:i:p1:s037843712200855x

DOI: 10.1016/j.physa.2022.128297

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