Machine Learning Meets Quantum Physics - Lecture Notes in Physics
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TOTAL DOCUMENTS

21

(FIVE YEARS 21)

H-INDEX

2

(FIVE YEARS 2)

Published By Springer International Publishing

9783030402440, 9783030402457

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Building Nonparametric n-Body Force Fields Using Gaussian Process Regression

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_5 ◽

2020 ◽

pp. 67-98

Author(s):

Aldo Glielmo ◽

Claudio Zeni ◽

Ádám Fekete ◽

Alessandro De Vita

Keyword(s):

Gaussian Process ◽

Force Fields ◽

Gaussian Process Regression

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Molecular Dynamics with Neural Network Potentials

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_12 ◽

2020 ◽

pp. 233-252 ◽

Author(s):

Michael Gastegger ◽

Philipp Marquetand

Keyword(s):

Neural Network ◽

Molecular Dynamics

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Introduction to Neural Networks

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_4 ◽

2020 ◽

pp. 37-62

Author(s):

Grégoire Montavon

Keyword(s):

Neural Networks

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Introduction to Material Modeling

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_2 ◽

2020 ◽

pp. 7-24

Author(s):

Jan Hermann

Keyword(s):

Material Modeling

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Message Passing Neural Networks

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_10 ◽

2020 ◽

pp. 199-214 ◽

Author(s):

Justin Gilmer ◽

Samuel S. Schoenholz ◽

Patrick F. Riley ◽

Oriol Vinyals ◽

George E. Dahl

Keyword(s):

Neural Networks ◽

Message Passing

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Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_17 ◽

2020 ◽

pp. 377-395

Author(s):

Rickard Armiento

Keyword(s):

Machine Learning ◽

High Throughput ◽

Materials Design ◽

Learning Models ◽

Machine Learning Models

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Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_9 ◽

2020 ◽

pp. 171-194

Author(s):

R. A. Vargas-Hernández ◽

R. V. Krems

Keyword(s):

Gaussian Processes ◽

Quantum Observables

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Generative Models for Automatic Chemical Design

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_21 ◽

2020 ◽

pp. 445-467 ◽

Author(s):

Daniel Schwalbe-Koda ◽

Rafael Gómez-Bombarelli

Keyword(s):

Generative Models ◽

Chemical Design

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Learning Representations of Molecules and Materials with Atomistic Neural Networks

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_11 ◽

2020 ◽

pp. 215-230

Author(s):

Kristof T. Schütt ◽

Alexandre Tkatchenko ◽

Klaus-Robert Müller

Keyword(s):

Neural Networks

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Machine Learning for Molecular Dynamics on Long Timescales

Machine Learning Meets Quantum Physics - Lecture Notes in Physics ◽

10.1007/978-3-030-40245-7_16 ◽

2020 ◽

pp. 331-372 ◽

Author(s):

Frank Noé

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Long Timescales

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