The sci-kit learn algorithms were ridge regression, kernel ridge regression (KRR) with a radial basis function (RBF) kernel, and support vector regression (SVR) with the RBF kernel. The models were hyperparameter tuned and performance was monitored on a validation set. The final model performance was evaluated on a held-out test set. For a full description of the ML procedure see ESI, section 4.