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A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck +2
Phonon-based approaches and molecular dynamics are widely established methods for gaining access to a temperature-dependent description of material properties. However, when a comp…
Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram
N. Unglert, J. Carrete, L. B. Pártay +1
Nested sampling is a promising method for calculating phase diagrams of materials, however, the computational cost limits its applicability if ab-initio accuracy is required. In th…
Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of θ-TaN
Ashis Kundu, Yani Chen, Xiaolong Yang +5
Recent theoretical and experimental research suggests that -TaN is a semimetal with high thermal conductivity (), primarily due to the contribution of phonons ($κ_\texttt{ph}…
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
Emanuele Bosoni, Louis Beal, Marnik Bercx +42
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter phys…
Accurate effective harmonic potential treatment of the high-temperature cubic phase of Hafnia
Sebastian Bichelmaier, Jesús Carrete, Michael Nelhiebel +1
HfO is an important high- dielectric and ferroelectric, exhibiting a complex potential energy landscape with several phases close in energy. It is, however, a strongly anhar…
Evolutionary computing and machine learning for the discovering of low-energy defect configurations
Marco Arrigoni, Georg K. H. Madsen
Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challen…