Machine Learning Meets Quantum Physics
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- Artikel-Nr.: 10398140
Beschreibung
Introduction to Material Modeling.- Kernel Methods for Quantum Chemistry.- Introduction to Neural Networks.- Building nonparametric n-body force fields using Gaussian process regression.- Machine-learning of atomic-scale properties based on physical principles.- Quantum Machine Learning with Response Operators in Chemical Compound Space.- Physical extrapolation of quantum observables by generalization with Gaussian Processes.- Message Passing Neural Networks.- Learning representations of molecules and materials with atomistic neural networks.- Molecular Dynamics with Neural Network Potentials.- High-Dimensional Neural Network Potentials for Atomistic Simulations.- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights.- Active learning and Uncertainty Estimation.- Machine Learning for Molecular Dynamics on Long Timescales.- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design.- Polymer Genome: A polymer informatics platform to accelerate polymer discovery.- Bayesian Optimization in Materials Science.- Recommender Systems for Materials Discovery.- Generative Models for Automatic Chemical Design.
Eigenschaften
Breite: | 156 |
Gewicht: | 732 g |
Höhe: | 237 |
Länge: | 234 |
Seiten: | 467 |
Sprachen: | Englisch |
Autor: | Alexandre Tkatchenko, Klaus-Robert Müller, Koji Tsuda, Kristof T. Schütt, O. Anatole von Lilienfeld, Stefan Chmiela |
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