15 citations · 16 across the 3 of their papers we have counts for
3 papers
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…