Machine learning many-body potentials for colloidal systems

TL;DR

This paper introduces a machine learning approach to model many-body interactions in colloidal systems, significantly reducing computational costs while accurately capturing phase behavior and structure.

Contribution

The authors develop a machine learning framework that fits effective many-body potentials for colloids, enabling efficient and accurate simulations across different states.

Findings

ML potentials are effectively state-independent.

The method reduces computational cost by several orders of magnitude.

Accurately describes phase behavior and structure in colloid-polymer mixtures.

Abstract

Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions.