Within this dataset, each folder corresponds to a distinct project from the associated thesis (Computational Modelling and Machine Learning Approaches Towards Understanding Asymmetric Catalytic Organic Reactions): Aldehyde_allylboration: Hydroxyl carboxylic acid-catalysed allylboration of aldehydes Machine_learning: Machine learning activation barriers Mukaiyama aldol: Oxazaborolidinone-catalysed Mukaiyama aldol reactions Partial_transfer_hydrogenation: Phosphoric acid-catalysed partial transfer hydrogenation of naphthyridines S-H insertion: Thiourea-catalysed S-H insertion of sulfoxonium ylides These folders contain Gaussian09 and Gaussian16 output files for each project, as well as a document with all machine learning model metrics, features, and hyperparameters for the Machine_learning folder. Where relevant, within each project, files are sorted by level of theory. If no level of theory is specified, all files have the same level of theory (this can be found via the main text of the associated thesis). All structures are labelled as per their corresponding chemical structure, e.g. michael acceptor, complex, transition state, etc. With the exception of the Machine_learning project, structures are ordered in terms of their relative quasiharmonic free energies. For example, BHCA_Ar1_1.out corresponds to the lowest energy conformation of the BHCA catalyst (Ar1) from chapter "Hydroxyl carboxylic acid-catalysed allylboration of aldehydes". Structures appearing in the main text of the associated thesis are appended with an appropriate label (e.g., TS_R1_5_TS-2-1P.out corresponds to TS-2.1' (prime) in the text). Single point energy corrections are appended with "_SPE", for example "Full_catalyst_2_SPE.out".