8 papers · 1 filter
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 …
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…
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…
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…