Electrochemical Stability Window of Polymeric Electrolytes

The electrochemical stability window (ESW) is a fundamental consideration while choosing polymers for electrolytes in lithium-ion batteries. In this work, we propose two computational procedures, viz. first-principles density functional theory (DFT) computations coupled with classical molecular dynamics (MD) simulations and machine learning (ML) methods to efficiently and accurately estimate ESW of polymers electrolytes. Six model polymers were investigated, namely, polyethylene (PE), polyethylene oxide (PEO), polyvinyl alcohol (PVA), poly(methyl methacrylate) (PMMA), polycaprolactone (PCL) and polyvinyl chloride (PVC). The role of polymer chemistry and the morphological complexity in determining ESW of these polymers have been elucidated. Comparison with established experimental values revealed that the ESW can be accurately predicted using DFT calculations coupled with MD simulations. However, this method is still time-consuming and the relevant force fields are limited, therefore ML methods are proposed to predict the ESW of single chain models with a first-order accuracy instantaneously. Overall, these two computational procedures proposed in this work can assist the rational design of novel solid polymer electrolytes with desired ESW values.