activity
20102026
most citedShortcomings of meta-GGA functionals when describing magnetism

55 citations · 123 across the 25 of their papers we have counts for

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cond-mat.mtrl-sci2026

Molecular-dynamics-based modal analysis of heat transport in quasi-one-dimensional systems from a symmetry-adapted perspective

Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1

Detailed analysis of thermal conductivity results obtained from molecular dynamics (MD) trajectories conventionally relies on knowledge of the harmonic vibrational modes of the sys…

cond-mat.mtrl-sci20251 cited

Structural Optimization in Tensor LEED Using a Parameter Tree and -Factor Gradients

Alexander M. Imre, Paul Haidegger, Florian Kraushofer +10

Quantitative low-energy electron diffraction [LEED ] is a powerful method for surface-structure determination, based on a direct comparison of experimentally observed

cond-mat.mtrl-sci2025

Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems

Sandro Wieser, YuJie Cen, Georg K. H. Madsen +1

Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to eq…

cond-mat.mtrl-sci2025

Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective

Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1

Due to their aspect ratio and wide range of thermal conductivities, nanotubes hold significant promise as heat-management nanocomponents. Their practical use is, however, often lim…

cond-mat.mtrl-sci2024

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study

Elin Dypvik Sødahl, Jesús Carrete, Georg K. H. Madsen +1

Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to prop…

cond-mat.mtrl-sci2024

Machine-learning potential for phonon transport in AlN with defects in multiple charge states

Ying Dou, Koji Shimizu, Jesús Carrete +2

Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transpo…