Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"

This dataset contains the Gaussian 16 output files for the dataset of aza-Michael addition reactions used in the publication "Fast Identification of Reactions with Desired Barriers by Reformulating Machine Learning Activation Energies". The structures of the methylamine nucleophile, the 1000 Michael acceptor electrophiles and their 1000 transition states were all optimised at the wB97X-D/def2-TZVP level of theory with the IEFPCM(water) implicit solvent model. Before optimisation all Michael acceptors and transition states were conformationally searched using the MMFF force field in Schrödinger's MacroModel software and the lowest energy conformer was selected for DFT calculation. This dataset also contains the Gaussian 16 output files for the SVWN/def2-SVP single-point energy calculations on the dihydrogen activation catalyst and transition state structures.