15 citations · 16 across the 3 of their papers we have counts for
4 papers
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…
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…
Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters
Niklas Frederik Schmitz, Bruno Ploumhans, Michael F. Herbst
We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-funct…
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data…