Mechanism behind the variation of oxidation-reduction potential of lithium metal revealed by introducing data science combined with computational calculations (IMAGE)
Caption
The relative importance of descriptors for the oxidation-reduction potential of lithium metal was obtained from partial least square (PLS) regression analysis. The correlation between the predicted and observed true values of the oxidation-reduction potential of lithium metal is well fitted, which is shown as an inset figure, along with the root mean squared error (RMSE). A numerous data related to the solution structure and physicochemical properties of electrolytes were collected by MD and DFT computational calculations, and their effect to the oxidation-reduction potential of lithium metal has been quantitatively analyzed with machine learning-based regression analysis. A specific factor, coordination state of Li+ and anion FSI-, has been revealed as a most important descriptor to determining the oxidation-reduction potential of lithium metal.
Credit
Yamada & Kitada Lab., Department of Chemical System Engineering, The University of Tokyo
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