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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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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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Interpretable deep neural networks for single-trial EEG classification

Journal of neuroscience methods, 2016-12, Vol.274, p.141-145

2016

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Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence

Journal of chemical theory and computation, 2023-07, Vol.19 (14), p.4619-4630

2023

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Grundlinien zum Systeme der Aesthetik‹ (1824) und andere kunstphilosophische Schriften

Johan Ludvig Heiberg / Klaus Müller-Wille

2022

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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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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

The journal of physical chemistry letters, 2022-11, Vol.13 (43), p.10183-10189

2022

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Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

The Journal of chemical physics, 2020-05, Vol.152 (19), p.194106-194106

2020

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Asymptotically unbiased estimation of physical observables with neural samplers

Physical review. E, 2020-02, Vol.101 (2-1), p.023304-023304, Article 023304

2020

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A lower limb exoskeleton control system based on steady state visual evoked potentials

Journal of neural engineering, 2015-10, Vol.12 (5), p.056009-056009

2015

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Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation

The Journal of pathology, 2022-04, Vol.256 (4), p.378-387

2022

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The Formation of Residual Lithium Compounds on Ni‐Rich NCM Oxides: Their Impact on the Electrochemical Performance of Sulfide‐Based ASSBs

Advanced functional materials, 2024-05, Vol.34 (21), p.n/a

2024

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Finding stationary subspaces in multivariate time series

Physical review letters, 2009-11, Vol.103 (21), p.214101-214101, Article 214101

2009

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Superparamagnetic Luminescent MOF@Fe3O4/SiO2 Composite Particles for Signal Augmentation by Magnetic Harvesting as Potential Water Detectors

ACS applied materials & interfaces, 2016-03, Vol.8 (8), p.5445-5452

2016

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Nachruf auf Rudhard Klaus Müller (1936–2021)

Nachrichten aus der Chemie, 2022-03, Vol.70 (3), p.86-86

2022

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Two Series of Lanthanide Coordination Polymers and Complexes with 4′‐Phenylterpyridine and their Luminescence Properties

European journal of inorganic chemistry, 2019-11, Vol.2019 (42), p.4564-4571

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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A mathematical model for the two-learners problem

Journal of neural engineering, 2017-06, Vol.14 (3), p.036005-036005

2017

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Die Stellung des Handwerks innerhalb der Gesamtwirtschaft

Müller, Klaus

2017

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Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Journal of neural engineering, 2016-08, Vol.13 (4), p.046003-046003

2016

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Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope

Journal of physical chemistry. C, 2023-07, Vol.127 (28), p.13817-13836

2023

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An explainable AI framework for robust and transparent data-driven wind turbine power curve models

Energy and AI, 2024-01, Vol.15, p.100328, Article 100328

2024

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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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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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Strukturentwicklungen im Handwerk

Müller, Klaus

2015

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Machine Learning for Molecular Simulation

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

Interpretable deep neural networks for single-trial EEG classification

Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence

Grundlinien zum Systeme der Aesthetik‹ (1824) und andere kunstphilosophische Schriften

Machine learning of solvent effects on molecular spectra and reactions

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

Asymptotically unbiased estimation of physical observables with neural samplers

A lower limb exoskeleton control system based on steady state visual evoked potentials

Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation

The Formation of Residual Lithium Compounds on Ni‐Rich NCM Oxides: Their Impact on the Electrochemical Performance of Sulfide‐Based ASSBs

Finding stationary subspaces in multivariate time series

Superparamagnetic Luminescent MOF@Fe3O4/SiO2 Composite Particles for Signal Augmentation by Magnetic Harvesting as Potential Water Detectors

Nachruf auf Rudhard Klaus Müller (1936–2021)

Two Series of Lanthanide Coordination Polymers and Complexes with 4′‐Phenylterpyridine and their Luminescence Properties

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

A mathematical model for the two-learners problem

Die Stellung des Handwerks innerhalb der Gesamtwirtschaft

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope

An explainable AI framework for robust and transparent data-driven wind turbine power curve models

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

Unmasking Clever Hans predictors and assessing what machines really learn

Strukturentwicklungen im Handwerk

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