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
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...
Jörg Behler, Michele Ceriotti, Cecilia Clementi et al.· 0 citations
This work presents the first exploration of multivariate conformal methods for chemical properties, including Bonferroni-corrected hyperrectangles, hyperellipsoidal sets based on the Mahalanobis distance, and custom loss functions within conformal risk control.
Katharine Fisher, M. Herbst, J. Kermode et al.· 0 citations
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
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