Work

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes

Year: 2021

Type: article

Abstract: Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials. Notwithstanding this, the structure of concentrated electrolytes remains enigmatic. Many theoretical approaches attempt to model the concentrated electrolyte by introducing the idea of ion pairs, with ions either being tightly "paired" with a counter-ion or "free" to screen charge. In this study, we reframe the problem into the language of computational statistics and test the null hypothesis that all ions share the same local environment. Applying the framework to molecular dynamics simulations, we find that this null hypothesis is not supported by data. Our statistical technique suggests the presence of two distinct local ionic environments at intermediate concentrations, whose differences surprisingly originate in like charge correlations rather than unlike charge attraction. Through considering the effect of these "aggregated" and "non-aggregated" states on bulk properties including effective ion concentration and dielectric constant, we identify a scaling relation between the effective screening length and theoretical Debye length, which applies across different dielectric constants and ion concentrations.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0039617

Authors: Penelope Jones, Fabian Coupette, Andreas Härtel, Alpha A. Lee

Institutions: University of Cambridge, University of Freiburg

Language: English

Topic: Electrostatics and Colloid Interactions

Subfield: Physical and Theoretical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funders: Deutsche Forschungsgemeinschaft, Engineering and Physical Sciences Research Council