These approaches based on high-throughput quantum chemical calculations, while sometimes very helpful (see Fig. ), are not a panacea: they are limited to molecules and properties which can be computed to a reasonable accuracy with quantum chemistry with the computer resources available. In particular it is usually impractical to compute large numbers of macromolecules, or molecules with complicated electronic structures (e.g. many metal complexes), so the quantum chemistry data sets are biased towards small simple molecules. Even for relatively small simple molecules, it can be impossible to guarantee that one has found all the important (i.e. low energy) conformers. We also do not know how to compute some important properties (e.g. solvation of ions, photochemistry in condensed phase) to the accuracy required for predictive kinetic modeling. And in some cases, such as reaction barriers, we believe we know how to do the quantum chemistry calculations, but it can be challenging to determine the barrier heights by experiment to an accuracy sufficient to really test if the quantum chemistry is accurate. All of these issues lead to important gaps and errors in the training datasets for any rate estimator, and can force the estimator to work in extrapolation (e.g. to estimate reactions of large molecules from training data that only includes small molecules), leading to uncontrolled errors in the estimates.