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Ion transport in concentrated electrolytes plays a fundamental role inelectrochemical systems such as lithium ion batteries. Nonetheless, themechanism of transport amid strong ion-ion interactions remains enigmatic. Akey question is whether the dynamics of ion transport can be predicted by thelocal static structure alone, and if so what are the key structural motifs thatdetermine transport. In this paper, we show that machine learning cansuccessfully decompose global conductivity into the spatio-temporal average oflocal, instantaneous ionic contributions, and relate this ``local molarconductivity" field to the local ionic environment. Our machine learning modelaccurately predicts the molar conductivity of electrolyte systems that were notpart of the training set, suggesting that the dynamics of ion transport ispredictable from local static structure. Further, through analysing thismachine-learned local conductivity field, we observe that fluctuations in localconductivity at high concentration are negatively correlated with total molarconductivity. Surprisingly, these fluctuations arise due to a long taildistribution of low conductivity ions, rather than distinct ion pairs, and arespatially correlated through both like- and unlike-charge interactions. Morebroadly, our approach shows how machine learning can aid the understanding ofcomplex soft matter systems, by learning a function that attributes globalcollective properties to local, atomistic contributions.

Inferring global dynamics from local structure in liquid electrolytes