Titlebook: Machine Learning Meets Quantum Physics; Kristof T. Schütt,Stefan Chmiela,Klaus-Robert Müll Book 2020 The Editor(s) (if applicable) and The

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Alexander Shapeev,Konstantin Gubaev,Evgenii Tsymbalov,Evgeny Podryabinkin stellt die Qualifizierung zum Dozent im Rettungsdienst dar, der vorwiegend an Rettungsdienstschulen t?tig ist. Auch für diese Personengruppe ist das Werk hervorragend geeignet..978-3-642-34939-3978-3-642-34940-9

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LITHE

Introduction to Neural Networksresent practical steps that ease training of neural networks, and then review simple approaches to introduce prior knowledge into the model. The discussion is supported by theoretical arguments as well as examples showing how well-performing neural networks can be implemented easily in modern neural network frameworks.

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Message Passing Neural Networksph data fit into this framework. This chapter contains large overlap with Gilmer et al. (International Conference on Machine Learning, pp. 1263–1272, 2017), and has been modified to highlight more recent extensions to the MPNN framework.

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High-Dimensional Neural Network Potentials for Atomistic Simulationspotential energy surface exactly, are used as descriptors for the atomic environments. This chapter describes how such symmetry functions and high-dimensional neural network potentials are constructed and validated.

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Book 2020 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-

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