Machine Learning Meets Quantum Physics [E-Book] / edited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller.
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 w...
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Personal Name(s): | Chmiela, Stefan, editor |
Müller, Klaus-Robert, editor / Schütt, Kristof T., editor / Tkatchenko, Alexandre, editor / Tsuda, Koji, editor / von Lilienfeld, O. Anatole, editor | |
Edition: |
1st edition 2020. |
Imprint: |
Cham :
Springer,
2020
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Physical Description: |
XVI, 467 pages 137 illustrations, 125 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783030402457 |
DOI: |
10.1007/978-3-030-40245-7 |
Series Title: |
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Lecture Notes in Physics ;
968 |
Subject (LOC): |
- 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.