株式会社極東書店トップ商品一覧Machine Learning and Data-Driven Research in Chemistry: Concepts, Techniques, and Applications.

商品詳細

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

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

・ISBN 978-1-119-31091-4 hard US$ 150.00

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お気に入り
著者・編者Hachmann, Andreas,
出版社 (Wiley-Blackwell (an imprint of John Wiley & Sons Ltd), UK)
出版年月2021
ページ数524 pp.
言語ENG
ニュース番号<A00-11982>

解説

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