One way to avoid the need to purchase or synthesize a large number of molecules, and then perform thousands of experiments, is to instead start with a large number of quantum chemistry calculations. Pre-training a solvation model with many quantum chemical (COSMO-RS ) calculations, and then calibrating that model with ~200 solute-solvent pairs gives (Fig. , blue curve) about the same prediction accuracy as the purely experimental model (orange curve) trained with ~3,000 solute-solvent pairs. This approach has the important advantage that one can easily compute molecules which are not purchasable, hard to synthesize, or short-lived. My group has constructed several large quantum chemistry datasets , and used them and the available experimental data to train estimators for several different quantities important in kinetics: thermochemistry of both stable molecules and radicals, reaction barriers, transition state geometries, gas-phase k's, solvent effects on k's and Keq's, and IR and UV/vis spectra. In recent years, it has become possible to use the computer to automatically discover transition state geometries and so to compute reaction barriers for large numbers of reactions. . With these large data sets, one can use machine-learning methods to build models for rapidly estimating the barriers for other reactions. A particularly interesting example of this approach is Spiekermann et al.'s development of a fast estimator for predicting unimolecular reaction barriers (for neutral singlet PES's involving only a few first row elements) trained by CCSD(T)-F12 data, which achieves an accuracy better than possible with DFT calculations, Fig. .