Training strategy. All Gaussian Approximation Potentials (GAPs) were trained using the gap_fit and QUIP codes with a Smooth Overlap of Atomic Positions (SOAP) kernel with hyperparameters as defined in Table . The relationship between the size of the training set required to obtain an accurate GAP scales with system size is detailed for linear alkanes and small organic molecules in SI §S5. GAP-MD simulations were performed with ASE 32 interfaced to QUIP with the quippy wrapper using the Langevin integrator. Initial configurations, CUR selection and automated training set construction were generated with the mlp-train Python package (v. 1.0.0a). The training set developed by AL was based on MLP-dynamics with the energy selection strategy 'diff', E T = 2.3 kcal mol -1 (0.1 eV). The other AL selection strategies, based on SOAP similarity, are discussed in SI §S7.