The final category of submissions were computational protocols which made use in one way or the other of learning strategies, either to correct computed harmonic frequencies, or to overall predict the position of the bands in the hydrate complexes. The worst performing set of values under this category was LS2, with very large deviations for MLA, THT and TFE, 99, 88 and 166 cm -1 absolute errors respectively. We will discuss this set of values together with one of the best performing sets (LS3), since these originate from the same group and make use of the same training data. This consisted of the training and test set molecules, expanded by over 200 other organic molecules retrieved from the GDB11 dataset. The latter were chosen based on a similarity search, in an attempt to better cover the chemical space of the test set. The harmonic frequencies for the full set of hydrates were calculated at the B3LYP-D3(BJ)/aug-cc-pVTZ level of computation, which together with the measured hydrate frequencies (when available) served as data for both LS2 and LS3 learning approaches. In the case of LS2 (the worst performing submission in the LS category) a kernel was trained to reproduce the harmonic frequencies. The descriptors were generated from the Faber-Christensen-Huang-Lilienfeld (FCHL19) representation (total length 33 by 486). Using the same representation, the harmonic values were corrected to the known (experimental) training set values following the ∆-learning method. The two kernels were used in combination to arrive at the final predicted values, using the experimental O-H symmetric stretch wavenumbers for water (3657 cm -1 ). In the case of LS3, the same underlying data was used, but this time a neural network was trained on the basis of the optimised geometries to provide the corresponding harmonic wavenumbers. The descriptors were also based upon FCHL19 (total length 193 after reduction by a principal component analysis, as described in the respective Supplementary Information). The test set molecules were included in the training of the aforementioned neural network. Transfer learning was then used to replicate the experimental values for 9 hydrate complexes of the training set (the radical case was excluded). Further details on the architecture and algorithms used are provided in the respective Supplementary Information. As one can observe comparing the LS2 and LS3 values, the transfer learning strategy was far more successful than the kernel approach. However, one should not necessarily pin this on the methods. After disclosing the results, a re-assessment of LS2 revealed that there was a mistake in the scaling of the standardised predictions. Instead of the anharmonic average and variance, the harmonic values were used, leading to an overestimation of the fundamentals values. With the correct procedure the root mean squared difference decreases from 74 to 54 cm -1 . For further details, see "Re-evaluation of the Hydra results for Method LS2 (∆-learning)" in the ESI †. It would be interesting to revisit both models with knowledge of the final target quantities. The other two submissions (LS1 and LS4) approached the problem making use of computed harmonic frequencies corrected by an empirical function. In the case of LS1, the base harmonic predictions were obtained at the PBE-D3 level of theory using the projector augmented wave approach (PAW). For calculation details please consult the corresponding ESI †. Eight complexes from the training set were used to fit a linear regression formula (two were excluded due to issues in the optimisation), the latter being applied to the harmonic predictions of the test set. The training and test sets roughly covered the same range of OH shifts, with similar types of interactions, providing a reasonable basis for the correction function. Albeit LS1 was not among the top group of submissions, the RMSD was still overall below 30 cm -1 . Lastly, LS4 used a somewhat more complex linear correction function. The geometries and corresponding harmonic wavenumbers were obtained with the PBE0-D3(BJ) functional. The basis sets used were may-cc-pVTZ for the organic molecules, aug-cc-pVQZ for the water molecule in the complex. An empirical linear function was then used to correct the harmonic predictions to target experimental values. The training data consisted of 42 hydrate complexes with published spectroscopic data. The linear function included two parameters which were fitted to the aforementioned data, weighing in the O-H bond elongation. Further details are provided in the corresponding Supplementary Information. The performance of this approach was very similar to that of LS3.