Machine learning many-body potentials for charged colloids in primitive 1:1 electrolytes

Abstract

Effective interactions between charged particles dispersed in an electrolyte are most commonly modeled using the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential, where the ions in the suspension are coarse-grained out at mean-field level. However, several experiments point to shortcomings of this theory, as the distribution of ions surrounding colloids is governed by nontrivial correlations in regimes of strong Coulomb coupling (e.g. low temperature, low dielectric constant, high ion valency, high surface charge). Insight can be gained by explicitly including the ions in simulations of these colloidal suspensions, even though direct simulations of dispersions of highly charged spheres are computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to generate density-dependent ML potentials that accurately describe the effective colloid interactions at given system parameters. These ML potentials enable fast simulations and make large-scale simulations of charged colloids in suspension possible, opening the possibility for a systematic study of their phase behaviour, in particular gas-liquid and fluid-solid coexistence.