Machine Learning in Chemistry

Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. Within the field of computational materials science, such an abundance of data is possible mainly due to the success of density functional theory (DFT) and High throughput (HT) methods. This article aims to show how Machine Learning approaches to modern computational chemistry are being used to uncover complexities in different fields.