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Unmasking Clever Hans predictors and assessing what machines really learn

Nature communications, 2019-03, Vol.10 (1), p.1096-1096, Article 1096

2019

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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

The journal of physical chemistry letters, 2015-06, Vol.6 (12), p.2326-2331

2015

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Machine Learning for Molecular Simulation

Annual review of physical chemistry, 2020-04, Vol.71 (1), p.361-390

2020

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SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Nature communications, 2021-12, Vol.12 (1), p.7273-7273, Article 7273

2021

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Methods for interpreting and understanding deep neural networks

Digital signal processing, 2018-02, Vol.73, p.1-15

2018

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Towards exact molecular dynamics simulations with machine-learned force fields

Nature communications, 2018-09, Vol.9 (1), p.3887-10, Article 3887

2018

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Chemical reviews, 2021-08, Vol.121 (16), p.9816-9872

2021

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Inverse design of 3d molecular structures with conditional generative neural networks

Nature communications, 2022-02, Vol.13 (1), p.973-11, Article 973

2022

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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

PloS one, 2015-07, Vol.10 (7), p.e0130140-e0130140

2015

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Bypassing the Kohn-Sham equations with machine learning

Nature communications, 2017-10, Vol.8 (1), p.872-10, Article 872

2017

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Quantum chemical accuracy from density functional approximations via machine learning

Nature communications, 2020-10, Vol.11 (1), p.5223-5223, Article 5223

2020

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Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules

Journal of chemical theory and computation, 2018-06, Vol.14 (6), p.2991-3003

2018

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Machine Learning Force Fields

Chemical reviews, 2021-08, Vol.121 (16), p.10142-10186

2021

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sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Computer physics communications, 2019-07, Vol.240, p.38-45

2019

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"What is relevant in a text document?": An interpretable machine learning approach

PloS one, 2017-08, Vol.12 (8), p.e0181142-e0181142

2017

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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Journal of chemical theory and computation, 2013-08, Vol.9 (8), p.3404-3419

2013

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Single-trial analysis and classification of ERP components — A tutorial

NeuroImage (Orlando, Fla.), 2011-05, Vol.56 (2), p.814-825

2011

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A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

PloS one, 2017-02, Vol.12 (2), p.e0172578-e0172578

2017

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Explaining the unique nature of individual gait patterns with deep learning

Scientific reports, 2019-02, Vol.9 (1), p.2391, Article 2391

2019

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Introduction to machine learning for brain imaging

NeuroImage (Orlando, Fla.), 2011-05, Vol.56 (2), p.387-399

2011

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Machine learning of solvent effects on molecular spectra and reactions

Chemical science (Cambridge), 2021-09, Vol.12 (34), p.11473-11483

2021

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Scientific reports, 2020-04, Vol.10 (1), p.6423, Article 6423

2020

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Selbstgewiss Ins Ungewisse: Auftragskommunikation in der Krise

1st Aufl 2021st edition, 2021

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Exploring chemical compound space with quantum-based machine learning

Nature reviews. Chemistry, 2020-07, Vol.4 (7), p.347-358

2020

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Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

Nature communications, 2021-01, Vol.12 (1), p.442-442, Article 442

2021

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Unmasking Clever Hans predictors and assessing what machines really learn

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Machine Learning for Molecular Simulation

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Methods for interpreting and understanding deep neural networks

Towards exact molecular dynamics simulations with machine-learned force fields

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Inverse design of 3d molecular structures with conditional generative neural networks

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bypassing the Kohn-Sham equations with machine learning

Quantum chemical accuracy from density functional approximations via machine learning

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules

Machine Learning Force Fields

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

"What is relevant in a text document?": An interpretable machine learning approach

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Single-trial analysis and classification of ERP components — A tutorial

A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

Explaining the unique nature of individual gait patterns with deep learning

Introduction to machine learning for brain imaging

Machine learning of solvent effects on molecular spectra and reactions

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Selbstgewiss Ins Ungewisse: Auftragskommunikation in der Krise

Exploring chemical compound space with quantum-based machine learning

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

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