Fig. 6
From: Advancing material property prediction: using physics-informed machine learning models for viscosity
QSPR performance on six battery-relevant solvents. Predictions of descriptor-based LGBM model and GNN-based EdgePool model when using two-dimensional descriptors (2D), molecular dynamics (MD) descriptors, or combinations of 2D and MD (2D and MD) in the QSPR models for six battery electrolytes: A methyl acetate (MA); B ethyl acetate (EA); C methyl butyrate (MB); D methyl propionate (MP); E dimethyl carbonate (DMC); and F ethyl methyl carbonate (EMC). Orange triangles represent experimental viscosities extracted from Ref [5]. MA, EA, MB, and MP are in the training set and contain the temperature ranges that encompass those found in Ref [5]. DMC is partially in the training set such that only two temperatures are provided to the models at T = 293.15 and 298.15 K. EMC is not within the training set at all. Molecular structures are drawn in the upper right of each plot