Inferring global dynamics from local structure in liquid electrolytes (2208.03182v1)

Abstract: Ion transport in concentrated electrolytes plays a fundamental role in electrochemical systems such as lithium ion batteries. Nonetheless, the mechanism of transport amid strong ion-ion interactions remains enigmatic. A key question is whether the dynamics of ion transport can be predicted by the local static structure alone, and if so what are the key structural motifs that determine transport. In this paper, we show that machine learning can successfully decompose global conductivity into the spatio-temporal average of local, instantaneous ionic contributions, and relate this ``local molar conductivity" field to the local ionic environment. Our machine learning model accurately predicts the molar conductivity of electrolyte systems that were not part of the training set, suggesting that the dynamics of ion transport is predictable from local static structure. Further, through analysing this machine-learned local conductivity field, we observe that fluctuations in local conductivity at high concentration are negatively correlated with total molar conductivity. Surprisingly, these fluctuations arise due to a long tail distribution of low conductivity ions, rather than distinct ion pairs, and are spatially correlated through both like- and unlike-charge interactions. More broadly, our approach shows how machine learning can aid the understanding of complex soft matter systems, by learning a function that attributes global collective properties to local, atomistic contributions.