arXiv subjects

Stefan Riemelmoser

Publications and source records attributed to Stefan Riemelmoser.

4 recordsLinked to original sources

arXiv2026

Lattice dynamics and structural phase stability of group-IV elemental solids with the r$^2$SCAN functional

The strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) functional is a milestone achievement of electronic structure theory. Recently, a revised and restored form (r$^2$SCAN) has been suggested as a replacement for SCAN in high-throughput applications. Here, we assess the accuracy and reliability of the r$^2$SCAN meta-GGA functional for the group-IV elemental solids carbon (C), silicon (Si), germanium (Ge), and tin (Sn). We show that the r$^2$SCAN functional agrees closely with its parent functional SCAN for elastic constants, bulk moduli, and phonon dispersions, but the numerical stability of r$^2$SCAN is superior. Both meta-GGA functionals outperform standard GGA (Perdew-Burke-Ernzerhof) in terms of accuracy and approach the level of common hybrid functionals (Heyd-Scuseria-Ernzerhof). However, we find that r$^2$SCAN performs much worse than SCAN for the $α\leftrightarrow β$ phase transition of both Ge and Sn, yielding larger phase energy differences and transition pressures.

cond-mat.mtrl-sci↗

arXiv2023

Machine learning density functionals from the random-phase approximation

Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we construct a DFT substitute functional for the RPA using supervised and unsupervised machine learning (ML) techniques. Our ML-RPA model can be interpreted as a non-local extension to the standard gradient approximation. We train an ML-RPA functional for diamond surfaces and liquid water and show that ML-RPA can outperform the standard gradient functionals in terms of accuracy. Our work demonstrates how ML-RPA can extend the applicability of the RPA to larger system sizes, time scales and chemical spaces.

cond-mat.mtrl-sci↗

arXiv2023

Plane wave basis set correction methods for RPA correlation energies

Electronic correlation energies from the random-phase approximation converge slowly with respect to the plane wave basis set size. We study the conditions, under which a short-range local density functional can be used to account for the basis set incompleteness error. Furthermore, we propose a one-shot extrapolation scheme based on the Lindhard response function of the homogeneous electron gas. The different basis set correction methods are used to calculate equilibrium lattice constants for prototypical solids of different bonding types.

cond-mat.mtrl-sci↗

arXiv2023

Optimized effective potentials from the random-phase approximation: Accuracy of the quasiparticle approximation

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-correlation functional. The OEP from the random-phase approximation (RPA) has played an important role ever since the conception of the OEP formalism. However, the solution of the OEP equation is computationally fairly expensive and has to be done in a self-consistent way. So far, large scale solid state applications have therefore been performed only using the quasiparticle approximation (QPA), neglecting certain dynamical screening effects. We obtain the exact RPA-OEP for 15 semiconductors and insulators by direct solution of the linearized Sham-Schlüter equation. We investigate the accuracy of the QPA on Kohn-Sham band gaps and dielectric constants, and comment on the issue of self-consistency.

cond-mat.mtrl-sci↗