Machine Learning Meets Quantum Physics

Name: Machine Learning Meets Quantum Physics
Brand: Springer Nature Switzerland AG
SKU: 9783030402440
Price: 380.8 PLN
Availability: InStock

Autor: Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller

Język:

Oprawa: Miękka

Wydawca: Springer Nature Switzerland AG

Dostępność: Dostępna u dostawcy

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Designing molecules and materials with desired properties is an important prerequisite for advancing...

Informacje o książce

Autor

Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2020

strony

467

EAN

9783030402440

Enbook ID

25391839

Wydawca

Springer Nature Switzerland AG

Waga

735

Wymiary

155 x 235 x 27

Pełny opis

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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