Georg K. H. Madsen

Georg K. H. Madsen

Technische Universität Wien

H-index: 51

Europe-Austria

About Georg K. H. Madsen

Georg K. H. Madsen, With an exceptional h-index of 51 and a recent h-index of 34 (since 2020), a distinguished researcher at Technische Universität Wien, specializes in the field of Density Functional Theory, Boltzmann Transport Equation, Energy Materials, Oxide Surfaces, Machine-Learned Force Fields.

His recent articles reflect a diverse array of research interests and contributions to the field:

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

Quantitative Predictions of the Thermal Conductivity in Transition Metal Dichalcogenides: Impact of Point Defects in MoS2 and WS2 Monolayers

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Spatially resolved uncertainties for machine learning potentials

Machine learning boosted study of the thermal conductivity of Janus PtSTe van der Waals heterostructures

Supplementary Information for Electron-induced nonmonotonic pressure dependence of the lattice thermal conductivity of θ-TaN

Thermal conductivity reduction in highly-doped cubic SiC by phonon-defect and phonon-electron scattering

Supplemental Material: Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures

Georg K. H. Madsen Information

University

Position

___

Citations(all)

33570

Citations(since 2020)

14766

Cited By

24971

hIndex(all)

51

hIndex(since 2020)

34

i10Index(all)

111

i10Index(since 2020)

88

Email

University Profile Page

Technische Universität Wien

Google Scholar

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Georg K. H. Madsen Skills & Research Interests

Density Functional Theory

Boltzmann Transport Equation

Energy Materials

Oxide Surfaces

Machine-Learned Force Fields

Top articles of Georg K. H. Madsen

Title

Journal

Author(s)

Publication Date

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

Emanuele Bosoni

Louis Beal

Marnik Bercx

Peter Blaha

Stefan Blügel

...

2024/1

Quantitative Predictions of the Thermal Conductivity in Transition Metal Dichalcogenides: Impact of Point Defects in MoS2 and WS2 Monolayers

The Journal of Physical Chemistry C

Srinivasan Mahendran

Jesús Carrete

Andreas Isacsson

Georg KH Madsen

Paul Erhart

2024/1/18

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Computer Physics Communications

Ralf Wanzenböck

Florian Buchner

Péter Kovács

Georg KH Madsen

Jesús Carrete

2024/4/1

Spatially resolved uncertainties for machine learning potentials

Esther Heid

Johannes Schörghuber

Ralf Wanzenböck

Georg KH Madsen

2024/5/2

Machine learning boosted study of the thermal conductivity of Janus PtSTe van der Waals heterostructures

Physical Review B

Lijun Pan

Jesús Carrete

Zhao Wang

Georg KH Madsen

2024/1/16

Supplementary Information for Electron-induced nonmonotonic pressure dependence of the lattice thermal conductivity of θ-TaN

Ashis Kundu

Yani Chen

Xiaolong Yang

Fanchen Meng

Jesús Carrete

...

2024

Thermal conductivity reduction in highly-doped cubic SiC by phonon-defect and phonon-electron scattering

Materials Today Physics

Guijian Pang

Fanchen Meng

Yani Chen

Ankita Katre

Jesús Carrete

...

2024/2/3

Supplemental Material: Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures

Lijun Pan

Jesús Carrete

Zhao Wang

Georg KH Madsen

2024

LoGAN: Local generative adversarial network for novel structure prediction

Péter Kovács

Esther Heid

Georg KH Madsen

2024/4/9

Predicting platinum adatom geometries on hematite for single-atom catalysis

Florian Buchner

Ralf Wanzenböck

Jesús Carrete Montana

Georg Kent Hellerup Madsen

2023/3/30

Neural-network force field backed nested sampling: Study of the silicon phase diagram

Physical Review Materials

Nico Unglert

Jesús Carrete

Livia B Pártay

Georg KH Madsen

2023/12/20

Accelerated screening of Cu–Ga–Fe oxide semiconductors by combinatorial spray deposition and high-throughput analysis

Materials Advances

Maximilian Wolf

Georg KH Madsen

Theodoros Dimopoulos

2023

Evaluating the efficiency of power‐series expansions as model potentials for finite‐temperature atomistic calculations

International Journal of Quantum Chemistry

Sebastian Bichelmaier

Jesús Carrete

Georg KH Madsen

2023/6/5

Accelerated Search for Surface Reconstructions

Ralf Wanzenböck

Florian Buchner

Jesús Carrete Montana

Georg Kent Hellerup Madsen

2023

Errors and Uncertainty in Machine Learning Models

Esther Carina Heid

Charles McGill

Florence Vermeire

William H Green

Georg Kent Hellerup Madsen

2023/9/25

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions

Chemical Science

Esther Heid

Daniel Probst

William H Green

Georg KH Madsen

2023

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

The Journal of Chemical Physics

Jesús Carrete

Hadrián Montes-Campos

Ralf Wanzenböck

Esther Heid

Georg KH Madsen

2023/5/28

Neural-Network-Based Nested Sampling for Efficient Exploration of Configuration Space: A Silicon Case Study

Nico Unglert

Jesús Carrete Montana

Livia B Pártay

Georg Kent Hellerup Madsen

2023/2/12

Origin of the success of mGGAs for bandgaps

The Journal of Chemical Physics

Péter Kovács

Peter Blaha

Georg KH Madsen

2023/12/28

Fleur

Zenodo

Daniel Wortmann

Gregor Michalicek

Nadjib Baadji

Markus Betzinger

Gustav Bihlmayer

...

2023

See List of Professors in Georg K. H. Madsen University(Technische Universität Wien)

Co-Authors

H-index: 141
Flemming Besenbacher

Flemming Besenbacher

Aarhus Universitet

H-index: 106
David J. Singh

David J. Singh

University of Missouri

H-index: 94
P Blaha

P Blaha

Technische Universität Wien

H-index: 92
Bjørk Hammer

Bjørk Hammer

Aarhus Universitet

H-index: 78
Laurence D Marks

Laurence D Marks

Northwestern University

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