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Published by Cham, Springer International Publishing/Imprint: Springer., 2020
ISBN 10: 3030402460 ISBN 13: 9783030402464
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First Edition
1st ed. 2020. XVI, 467 p. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Lecture Notes in Physics, 968. Sprache: Englisch.
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367873842 ISBN 13: 9780367873844
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Language: English
Published by Routledge 2019-12-10, 2019
ISBN 10: 0367873842 ISBN 13: 9780367873844
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Hardcover. Condition: Fine. Book measures 24x16cm,324pp. Bound in original publishers laminated boards. Binding in fine condition, internally pages clean, A very good clean copy.
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Taschenbuch. Condition: Neu. Machine Learning Meets Quantum Physics | Kristof T. Schütt (u. a.) | Taschenbuch | Lecture Notes in Physics | xvi | Englisch | 2020 | Springer | EAN 9783030402440 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer International Publishing, 2020
ISBN 10: 3030402444 ISBN 13: 9783030402440
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume.To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials.The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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