Author

N. Schmitz

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Preprint Sep 2026

Density functional perturbation theory of meta-generalized gradient approximations using algorithmic differentiation

Density functional perturbation theory (DFPT) is an established framework for the computation of derivatives in plane-wave density functional theory. We present an implementation of DFPT for exchange-correlation (XC) functionals $E_\mathrm{xc}(\rho,\tau)$ that incorporate an explicit dependence on both the density $\rh...

Bruno Ploumhans, N. Schmitz, M. Herbst · 0 citations

#machine learning Preprint Aug 2026

Euclidean Fourier Neural Operators

Euclidean Fourier neural operators (EFNOs) are proposed as a domain-independent alternative to FNOs and can learn operators that act consistently across periodic domains of varying shape and size by parameterizing the spectral kernel as a continuous function of the physical wavevector.

Nathanael Bosch, N. Schmitz, M. Herbst · 0 citations