1 citations · 1 across the 7 of their papers we have counts for
9 papers
Symmetry-based modal analysis of heat transport in molecular dynamics of quasi-1D systems
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…
Pulgon-tools: A toolkit for analysing and harnessing symmetries in quasi-1D systems
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1
Pulgon-tools is an open-source software package providing building blocks for the analysis and modeling of quasi-one-dimensional (quasi-1D) periodic systems based on line-group the…
Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
Nico Unglert, Michael Ketter, Georg K. H. Madsen
Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can pr…
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 …
msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
Florian Buchner, Johannes Schörghuber, Nico Unglert +2
We present msmJAX, a Python package implementing the multilevel summation method with B-spline interpolation, a linear-scaling algorithm for efficiently evaluating electrostatic an…
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…
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…
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…
Neural-network-enabled molecular dynamics study of HfO phase transitions
Sebastian Bichelmaier, Jesús Carrete, Georg K. H. Madsen
The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab-initio MD is too resource-intensive and phenomena for whi…