The resulting trajectories were analysed using two approaches: direct prediction of the NMR results from the trajectories (described below) and a Markov State Modelling (MSM). These MSM analyses were performed using the PyEMMA 3.5.4 package. Two vectors describing each solvent molecule were extracted from the MD trajectories and used for featurization. This approach offers a dimensionality reduction by removing unnecessary atomic coordinates and significantly speeds up data processing . The time-lagged independent component analysis method (TICA) was used to decouple any fast vibration-like motions from the significant solvent reorientations. The resulting TICs were then used to derive Markov state models, using k-means clustering to discretise the data to a representative set of microstates. An appropriate MSM lag time was determined to be 5 fs (10 timesteps) for both the acetone and ethanol systems based on implied timescales plots. Perron-cluster-cluster analysis (PCCA+) was used to cluster the microstates into a limited number of macrostates. Chapman-Kolmogrov (CK) tests were used to validate the resulting Markov state models, and Transition-path theory (TPT) was applied to determine the flux between macrostates ; see Section 7 of the Supplementary Information for further details.