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- University of ViennaAT22 papers
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- Shenyang National Laboratory for Materials ScienceCN2 papers
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4 papers · 2 filters
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
Carla Verdi, Ferenc Karsai, Peitao Liu +2
Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however…
Phase Transitions of Zirconia: Machine-Learned Force Fields Beyond Density Functional Theory
Peitao Liu, Carla Verdi, Ferenc Karsai +1
We present an approach to generate machine-learned force fields (MLFF) with beyond density functional theory (DFT) accuracy. Our approach combines on-the-fly active learning and $Δ…
Electronic state unfolding for plane waves: energy bands, Fermi surfaces and spectral functions
David Dirnberger, Georg Kresse, Cesare Franchini +1
Modern computing facilities grant access to first-principles density-functional theory study of complex physical and chemical phenomena in materials, that require large supercell t…
Optimized effective potentials from the random-phase approximation: Accuracy of the quasiparticle approximation
Stefan Riemelmoser, Merzuk Kaltak, Georg Kresse
The optimized effective potential (OEP) method presents an unambiguous way to construct the Kohn-Sham potential corresponding to a given diagrammatic approximation for the exchange…