Machine Learning and Data–Driven Research in Chemistry: Concepts, Techniques, and Applications

This book introduces the conceptual foundations, state-of-the-art techniques, as well as concrete application examples of modern data science in the chemical context. There is a particular focus on the combination of data science and computational studies. Topics covered include: •Knowledge discovery in chemical data: elucidating structure-property relationships and governing principles in chemical systems through modern data science •Features, descriptors, and other representations of chemical systems. Feature selection, feature transformation, and dimension reduction techniques •Statistical and machine learning techniques in data analysis and data mining •Virtual high-throughput screening efforts, big data, and databases in the chemical context •Computational chemistry method developments driven by data science •Technical aspects and software solutions This book introduces the conceptual foundations, state-of-the-art techniques, as well as concrete application examples of modern data science in the chemical context. There is a particular focus on the combination of data science and computational studies. Topics covered include: •Knowledge discovery in chemical data: elucidating structure-property relationships and governing principles in chemical systems through modern data science •Features, descriptors, and other representations of chemical systems. Feature selection, feature transformation, and dimension reduction techniques •Statistical and machine learning techniques in data analysis and data mining •Virtual high-throughput screening efforts, big data, and databases in the chemical context •Computational chemistry method developments driven by data science •Technical aspects and software solutions