We then ran simulations with the fitted potential both in water solution and bound to leukotriene A4 hydrolase (see below). These latter simulations revealed a number of samples with very high energy when evaluated with the QM method. Therefore, 300 such configurations were added to the training set, and the potential refitted. Such iterative fitting has been used before, and is expected to be an important technique for creating transferable machine learning potentials. The new GAP model had a similar RMS error on the test sets (0.65 kcal mol -1 and 0.95 kcal mol -1 Å-1 for energies and forces, respectively) and was stable in subsequent simulations (see Results section). Both GAP models are available as Supporting Information (xml), which may be evaluated using the QUIP software 66 and LAMMPS. In what follows, we refer to the two versions of our machine learned intramolecular potentials as GAP-v1 and GAP-v2.