Georg K. H. Madsen

TU Wien — Austria

Materials Science · Physical Sciences

51h-index26.4kcitations246works2.72yr avg
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Research Topics

Advanced Thermoelectric Materials and Devices(69), Machine Learning in Materials Science(62), Thermal properties of materials(46), Advanced Chemical Physics Studies(28), Chalcogenide Semiconductor Thin Films(17)

Publications246 total

Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
npj Computational Materials·2026Open Access
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective
npj Computational Materials·2026Open Access
Accelerating First-Principles Molecular-Dynamics Thermal Conductivity Calculations for Complex Systems
Journal of Chemical Theory and Computation·2025· 1 citedOpen Access
Completing the hierarchy of rotational defects in monolayer MoS <sub>2</sub> through symmetry-aware evolutionary search
Physical Chemistry Chemical Physics·2025Open Access
Structural Optimization in Tensor LEED Using a Parameter Tree and $R$-Factor Gradients
arXiv (Cornell University)·2025Open Access
Influence of point defects on thermal conductivity in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>TiFe</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:msub> <mml:mi>Ni</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> <mml:mi>Sb</mml:mi> </mml:mrow> </mml:math> alloys
Physical Review Materials·2025Open Access
Steering Langevin dynamics towards transition states using collective-variable-free resampling
2025Open Access
msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
arXiv (Cornell University)·2025Open Access
How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces
arXiv (Cornell University)·2025Open Access
Research data for 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Research data for 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems
arXiv (Cornell University)·2025Open Access
The density isobar of water: A comparative study of vdW-DF-cx and RPBE-D3
The Journal of Chemical Physics·2025Open Access
Completing the Hierarchy of Rotational Defects inMonolayer MoS2 through Symmetry-Aware EvolutionarySearch
2025Open Access
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective
arXiv (Cornell University)·2025Open Access
Replica Exchange Nested Sampling
Journal of Chemical Theory and Computation·2025· 4 citedOpen Access
Computing anharmonic infrared spectra of polycyclic aromatic hydrocarbons using machine learning molecular dynamics
Monthly Notices of the Royal Astronomical Society·2025· 5 citedOpen Access
Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport
PRX Energy·2025· 3 citedOpen Access
The density isobar of water: A comparative study of vdW-DF-cx and RPBE-D3
2025Open Access
Bayesian Optimization of Spray Parameters for the Deposition of Ga<sub>2</sub>O<sub>3</sub>–Cu<sub>2</sub>O Heterojunctions
ACS Applied Energy Materials·2025· 3 citedOpen Access
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Frequent Co-authors

Bo B. Iversen(13), Jesús Carrete(8), Peter Blaha(7), Karlheinz Schwarz(6), Bjørk Hammer(6), Natalio Mingo(5), Ingo Opahle(5), Anders Bentien(4), Simon Johnsen(4), Ralf Drautz(4), Lara Ferrighi(3), Ankita Katre(3), Ambroise van Roekeghem(3), Gilles Dennler(3), Radosław Chmielowski(3), Stéphane Jacob(3), Lasse Bjerg(3), Sandip Bhattacharya(3), David J. Singh(2), Fabien Tran(2)