Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes
This work provides a new statistical approach to understand the complex structure of concentrated electrolytes, which is important for researchers developing energy storage and biomaterials.
This paper investigates the structure of concentrated electrolytes using a Bayesian unsupervised learning framework. It refutes the null hypothesis that all ions share the same local environment, revealing distinct local ionic environments driven by like-charge correlations rather than unlike-charge attraction. The fraction of particles in non-aggregated environments exhibits universal scaling across varying dielectric constants and ionic concentrations.
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 electrolytes 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 show that this null hypothesis is not supported by data. Our statistical technique suggests the presence of distinct local ionic environments; surprisingly, these differences arise in like charge correlations rather than unlike charge attraction. The resulting fraction of particles in non-aggregated environments shows a universal scaling behaviour across different background dielectric constants and ionic concentrations.