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.
Chmiela, Stefan, (editor)
Müller, Klaus-Robert, (editor) / Schütt, Kristof T., (editor) / Tkatchenko, Alexandre, (editor) / Tsuda, Koji, (editor) / von Lilienfeld, O. Anatole, (editor)
1st edition 2020.
Cham : Springer, 2020
XVI, 467 pages 137 illustrations, 125 illustrations in color (online resource)
Lecture Notes in Physics ; 968
Full Text
Table of Contents:
  • 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.