Quantum chemistry calculations are possible on many more molecules and reactions than are practical to address experimentally -this is a huge advantage over relying solely on experimental data to train an estimator. As a result, several large datasets of reaction barriers and thermochemistry have been computed in recent years , and several additional large datasets are expected to be published soon. However, there are often systematic gaps in the quantum chemistry datasets even if the dataset ostensibly covers the reaction one is trying to estimate. This can happen because the quantum chemistry fails to converge (or gives wrong values) for molecules or reactions with unusual electronic structures or geometries. So for example the widely used QM9 dataset omits some small organic species with highly strained structures, and Grambow et al. were unable to find satisfactory transition states for about 20% of the reactions they considered despite trying several different approaches. A few percent of Grambow's large set of successfully computed saddle points were later identified to have significant deviations in barrier heights from CCSD(T)-F12 values , presumably due to convergence problems or the inability of the DFT method employed to handle the electronic structures for those transition states. Also, many data sets are only computed using DFT or even lower-accuracy methods, not the expensive high-accuracy methods needed for quantitativelyaccurate predictions of rates or equilibria. Only a few of the data sets have included the bond-additivity or isodesmic reaction corrections needed to achieve thermochemistry close to experimental values, Fig. . These problems all propagate into errors in a rate or thermochemistry estimator trained using these data. Some of these problems can be hard to uncover, particularly if the estimator is pretty good and so predicts most k(T) accurately. In the worst case, a serious mis-prediction might not become apparent until years later, when new experimental data or a much better quantum chemistry calculation becomes available, demonstrating that the estimate and/or the original quantum chemistry calculation for a particular reaction was incorrect. Caveat emptor! Also, while quantum chemical calculations are often much faster than experiments, it is important to keep in mind that they are not zero-cost to society: someone is paying for the expensive supercomputer hardware and the electricity it is consuming to generate large data sets, and the associated greenhouse gas emissions are damaging the climate. Often, as in Fig. , there is limited value in pushing to larger and larger dataset sizes. So it is best practice to build models at the same time one is computing (or measuring) the data, to better assess the value of the next tranche of data relative to the cost of obtaining it.