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Postquench gap dynamics of two-band superconductors

Physical review. B, 2019-10, Vol.100 (14), Article 144513

2019

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Continuous ground-state degeneracy of classical dipoles on regular lattices

Physical review. B, 2019-07, Vol.100 (1), p.014426, Article 014426

2019

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Machine Learning Meets Quantum Physics

1st ed. 2020., 2020

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Enabling simulation at the fifth rung of DFT: Large scale RPA calculations with excellent time to solution

Computer physics communications, 2015-02, Vol.187 (C), p.120-129

2015

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Controlling competing orders via nonequilibrium acoustic phonons: Emergence of anisotropic effective electronic temperature

Physical review. B, 2018-01, Vol.97 (3), Article 035135

2018

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Learning Representations of Molecules and Materials with Atomistic Neural Networks

Machine Learning Meets Quantum Physics, p.215-230

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Assembly of Surface-Mounted Devices on Flexible Substrates by Isotropic Conductive Adhesive and Solder and Lifetime Characterization

Micromachines (Basel), 2022-08, Vol.13 (8), p.1240

2022

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Generative Models for Automatic Chemical Design

Machine Learning Meets Quantum Physics, p.445-467

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Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Machine Learning Meets Quantum Physics, p.277-307

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Message Passing Neural Networks

Machine Learning Meets Quantum Physics, p.199-214

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

Machine Learning Meets Quantum Physics, p.377-395

Armiento, Rickard

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Building Nonparametric n-Body Force Fields Using Gaussian Process Regression

Machine Learning Meets Quantum Physics, p.67-98

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Active Learning and Uncertainty Estimation

Machine Learning Meets Quantum Physics, p.309-329

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High-Dimensional Neural Network Potentials for Atomistic Simulations

Machine Learning Meets Quantum Physics, p.253-275

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Molecular Dynamics with Neural Network Potentials

Machine Learning Meets Quantum Physics, p.233-252

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Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Machine Learning Meets Quantum Physics, p.129-154

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Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

Machine Learning Meets Quantum Physics, p.171-194

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Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Machine Learning Meets Quantum Physics, p.99-127

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Kernel Methods for Quantum Chemistry

Machine Learning Meets Quantum Physics, p.25-36

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Machine Learning for Molecular Dynamics on Long Timescales

Machine Learning Meets Quantum Physics, p.331-372

Noé, Frank

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Quantum Machine Learning with Response Operators in Chemical Compound Space

Machine Learning Meets Quantum Physics, p.155-169

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Fortnet, a software package for training Behler-Parrinello neural networks

Computer physics communications, 2023-03, Vol.284, p.108580, Article 108580

2023

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Introduction to Material Modeling

Machine Learning Meets Quantum Physics, p.7-24

Hermann, Jan

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Introduction to Neural Networks

Machine Learning Meets Quantum Physics, p.37-62

Montavon, Grégoire

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Bayesian Optimization in Materials Science

Machine Learning Meets Quantum Physics, p.413-426

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Postquench gap dynamics of two-band superconductors

Continuous ground-state degeneracy of classical dipoles on regular lattices

Machine Learning Meets Quantum Physics

Enabling simulation at the fifth rung of DFT: Large scale RPA calculations with excellent time to solution

Controlling competing orders via nonequilibrium acoustic phonons: Emergence of anisotropic effective electronic temperature

Learning Representations of Molecules and Materials with Atomistic Neural Networks

Assembly of Surface-Mounted Devices on Flexible Substrates by Isotropic Conductive Adhesive and Solder and Lifetime Characterization

Generative Models for Automatic Chemical Design

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Message Passing Neural Networks

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

Building Nonparametric n-Body Force Fields Using Gaussian Process Regression

Active Learning and Uncertainty Estimation

High-Dimensional Neural Network Potentials for Atomistic Simulations

Molecular Dynamics with Neural Network Potentials

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Kernel Methods for Quantum Chemistry

Machine Learning for Molecular Dynamics on Long Timescales

Quantum Machine Learning with Response Operators in Chemical Compound Space

Fortnet, a software package for training Behler-Parrinello neural networks

Introduction to Material Modeling

Introduction to Neural Networks

Bayesian Optimization in Materials Science

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