Dataset for "Computational Modelling and Machine Learning Approaches Towards Understanding Asymmetric Catalytic Organic Reactions"

For several decades, chemical modelling methods such as density functional theory (DFT) have provided invaluable contributions to the understanding of asymmetric and catalytic reactions. However, machine learning (ML) models, once trained, could allow for much more rapid screening of chemical reactions. In the thesis associated with this dataset, research into two distinct approaches to understanding organic reactions - modelling and ML - are presented, including several examples of conventional modelling with DFT, as well as details of a new ML methodology that bridges the gap between semi-empirical quantum mechanical (SQM) methods and DFT. This repository contains Gaussian09 and Gaussian16 output files for all computed structures used in this work, as well as a document containing a complete list of all metrics, features, and hyperparameters for all computed ML models.