Machine-Learned Many-Body Potentials for Charged Colloids reveal Gas-Liquid Spinodal Instabilities only in the strong-coupling regime of Primitive Models
Thijs ter Rele, Ren\'e van Roij, Marjolein Dijkstra

TL;DR
This study uses machine learning to develop efficient potentials for simulating highly charged colloids, revealing that gas-liquid instabilities occur only under strong electrostatic coupling, not in weakly coupled regimes.
Contribution
The paper introduces a machine-learning framework to accurately model colloid interactions, enabling simulations at high valencies that were previously computationally infeasible.
Findings
Gas-liquid spinodal instabilities appear only in strongly coupled regimes.
ML potentials reproduce phase separation observed in primitive-model simulations.
Like-charge attractions are absent in weakly coupled regimes, aligning with experimental observations.
Abstract
Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas-liquid and gas-solid…
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Taxonomy
TopicsMachine Learning in Materials Science · Block Copolymer Self-Assembly · Material Dynamics and Properties
