Author

M. Herbst

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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

Preprint Sep 2026

Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls

Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem. Multiscale simulations introduce an additional challenge: robust uncertainty quantification across scales. Even within one scale, computat...

Katharine Fisher, M. Herbst, J. Kermode et al. · 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