72 citations · 232 across the 17 of their papers we have counts for
21 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
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 widespre…
Density functional perturbation theory of meta-generalized gradient approximations using algorithmic differentiation
Bruno Ploumhans, Niklas Frederik Schmitz, Michael F. Herbst
Density functional perturbation theory (DFPT) is an established framework for the computation of derivatives in plane-wave density functional theory (DFT). We present an implementa…
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode +1
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…
Euclidean Fourier Neural Operators
Nathanael Bosch, Niklas Frederik Schmitz, Michael F. Herbst
Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent of the grid resolution at whi…
Moreau-Yosida-based Kohn-Sham Inversion for Periodic Systems
Vebjørn H. Bakkestuen, Michael F. Herbst, Vegard Falmår +2
Density-potential inversion for periodic systems within Moreau-Yosida-regularised density-functional theory is investigated, both theoretically and numerically. We develop the fram…
Perspective on Moreau-Yosida Regularization in Density-Functional Theory
Markus Penz, Michael F. Herbst, Trygve Helgaker +1
Within density-functional theory, Moreau-Yosida regularization enables both a reformulation of the theory and a mathematically well-defined definition of the Kohn-Sham approach. It…