Machine Learning Meets Quantum Physics
indgår i Lecture Notes in Physics serien
- Indbinding:
- Paperback
- Sideantal:
- 467
- Udgivet:
- 4. juni 2020
- Udgave:
- 12020
- Størrelse:
- 234x154x30 mm.
- Vægt:
- 726 g.
- 8-11 hverdage.
- 20. november 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Machine Learning Meets Quantum Physics
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.
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