Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Penelope K. Jones, Kara D. Fong, Kristin A. Persson, Alpha A. Lee,
| Summary:
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.
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