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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…
Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Manuel Gallego, Sebastián Roca-Jerat, David Zueco +1
Machine-learning (ML) ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum…
Computing Anharmonic Infrared Spectra of Polycyclic Aromatic Hydrocarbons Using Machine-Learning Molecular Dynamics
Xinghong Mai, Zhao Wang, Lijun Pan +4
We introduce a machine learning molecular dynamics (MLMD) approach to calculate the anharmonic infrared (IR) absorption spectra of polycyclic aromatic hydrocarbons (PAHs), key carr…