Tkatchenko Alexandre (19 results)
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Machine Learning Meets Quantum Physics
Schütt, Kristof T. (EDT); Chmiela, Stefan (EDT); Von Lilienfeld, O. Anatole (EDT); Tkatchenko, Alexandre (EDT); Tsuda, Koji (EDT)
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Machine Learning Meets Quantum Physics
Schütt, Kristof T. (EDT); Chmiela, Stefan (EDT); Von Lilienfeld, O. Anatole (EDT); Tkatchenko, Alexandre (EDT); Tsuda, Koji (EDT)
Language: English
Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Language: English
Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Published by Springer 2020-06 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Machine Learning Meets Quantum Physics
Schütt, Kristof T. (EDT); Chmiela, Stefan (EDT); Von Lilienfeld, O. Anatole (EDT); Tkatchenko, Alexandre (EDT); Tsuda, Koji (EDT)
Language: English
Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Machine Learning Meets Quantum Physics
Schütt, Kristof T. (EDT); Chmiela, Stefan (EDT); Von Lilienfeld, O. Anatole (EDT); Tkatchenko, Alexandre (EDT); Tsuda, Koji (EDT)
Language: English
Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Language: English
Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Language: English
Published by Springer Nature 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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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
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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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 elect…rostatic 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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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer International Publishing Jun 2020 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 484 pp. Englisch.
Language: English
Published by Springer International Publishing 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
- Softcover
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides an in-depth referenced work on the physics-based machine learning techniques that model electronic and atomistic properties of matterHighly interdisciplinary, it focuses on diverse fi…elds of investigation such as physics, chemistry and ma.
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Published by Springer 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Condition: New. PRINT ON DEMAND pp. 490.
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Published by Springer, Springer Jun 2020 2020
Series: Lecture Notes in Physics, Book 193 of 241. Book 193 of 241 - Lecture Notes in Physics
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, forc…es 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 484 pp. Englisch.
