Elsevier Computational and Data-Driven Chemistry Using Artificial Intelligence: Fundamentals, Methods and Applications: 1

Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. Review Explores how artificial intelligence tools can be harnessed for a variety of applications in computational and data-driven chemistry From the Back Cover Offering the ability to process large or complex datasets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Computational and Data-Driven Chemistry Using Artificial Intelligence highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. The book explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Chapters provide foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field, and go on to explore various approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy, and finally, potential future trends in the field. Drawing on the knowledge of its expert team of global contributors, Computational and Data-Driven Chemistry Using Artificial Intelligence, Fundamentals, Methods and Applications is a fascinating insight into this rapidly developing field and a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. About the Author Takashiro Akitsu is a full Professor of Chemistry at Tokyo University of Science. He completed his under graduate school training (chemistry) at Osaka University, Japan and his graduate school training (physical & inorganic chemistry, especially coordination, crystal and bioinorganic chemistry) at Osaka University (Ph.D. 2000). Following positions at Keio University, Japan, and Stanford University, USA, he moved to his current affiliation in 2008. He has published almost 220 articles in peer-reviewed journals and has presented multiple posters at international exhibitions. Prof Akitsu has been a peer reviewer of many journals and acted as an organizing committee of several international conferences.