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Sinopsis
Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
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Chen Qu is currently a research associate of National Institute of Standards and Technology. His current research focuses on applying machine learning methods to predict important chemical properties such as gas chromatography retention indices and mass spectra. He received his Ph.D. at Emory University, where he conducted research primarily on machine learning potential energy surfaces, under the guidance of Prof. Joel Bowman.
Hanchao Liu is currently a machine learning engineer at Google. His work focuses on building large-scale machine learning infrastructures and platforms. Dr. Liu received his Ph.D. in computational chemistry at Emory University under the tutelage of Prof. Joel Bowman, where he applied computational and machine learning methods to study the vibrational dynamics and spectra of various forms of water.
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Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
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Machine Learning in Molecular Sciences
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Springer Verlag GmbH, 2024
ISBN 10: 3031371984
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Machine Learning in Molecular Sciences
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Springer International Publishing, Springer Nature Switzerland Okt 2024, 2024
ISBN 10: 3031371984
ISBN 13: 9783031371981
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences. 328 pp. Englisch. Nº de ref. del artículo: 9783031371981
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Machine Learning in Molecular Sciences
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Taschenbuch. Condición: Neu. Machine Learning in Molecular Sciences | Chen Qu (u. a.) | Taschenbuch | x | Englisch | 2024 | Springer | EAN 9783031371981 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 130174058
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Machine Learning in Molecular Sciences
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ISBN 10: 3031371984
ISBN 13: 9783031371981
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Taschenbuch. Condición: Neu. Neuware -Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch. Nº de ref. del artículo: 9783031371981
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Machine Learning in Molecular Sciences
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ISBN 10: 3031371984
ISBN 13: 9783031371981
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences. Nº de ref. del artículo: 9783031371981
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Machine Learning in Molecular Sciences (Challenges and Advances in Computational Chemistry and Physics)
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