#dftk — Public Fediverse posts

Our work is also described in a scientific highlight from the NCCR Marvel collaboration: https://nccr-marvel.ch/highlights/AD-DFPT-Herbst

#dftk #algorithmicdifferentiation #densityfunctionaltheory #dft

New publication https://doi.org/10.1038/s41524-025-01880-3

Our work on AD-DFPT, a unification of #automaticdifferentiation with linear response for #densityfunctionaltheory is published in npj Computational Materials. We show examples for #property predition, #uncertainty propagation, the design of #materials and #machinelearning of new #dft models. #condensedmatter #dftk

Yesterday our #cecam workshop on #uncertainty quantification (#uq)
for atomistic modelling and #machinelearning for #materials #modelling
and #chemistry started.

The opening contribution was by Niklas Schmitz from @MatMat
on #uq for #densityfuncitionaltheory:
https://github.com/niklasschmitz/tutorial-cecam-workshop-dftk-2025
building upon our recent preprint https://arxiv.org/abs/2509.07785.

#condensedmatter #physics #dftk

New preprint https://arxiv.org/abs/2511.06957

A #perspective discussing Moreau-Yosida (MY) techniques in #densityfunctionaltheory.
MY regularisation has enabled to import tools from #convexanalysis into #dft
providing a new mathematical understanding of the most important atomistic simulation approach
and new robust algorithms for Kohn-Sham #dft.

Thanks to my co-authors from the #hylleraas centre and #oslomet for insightful discussions.

#condensedmatter #quantumchemistry #numericalanalysis #dftk

New publication https://doi.org/10.1103/PhysRevB.111.205143

New algorithm for the #inverseproblem of Kohn-Sham #densityfunctionaltheory (#dft), i.e. to find the #potential from the #density.

Outcome of a fun collaboration of @herbst with the group of Andre Laestadius at #oslomet to derive first mathematical error bounds for this problem

#condensedmatter #planewave #numericalanalysis #convexanalysis #dftk

@schmitz (left) explaining his recent work on making #dftk algorithmically #differentiable at the #cecam workshop on #dft and #ai (https://www.cecam.org/workshop-details/1281). With his work derivatives of key density-functional theory quantities like forces or band structures wrt. model parameters can now be easily computed.

@schmitz (left) explaining his recent work on making #dftk algorithmically #differentiable at the #cecam workshop on #dft and #ai (https://www.cecam.org/workshop-details/1281). With his work derivatives of key density-functional theory quantities like forces or band structures wrt. model parameters can now be easily computed.

@schmitz (left) explaining his recent work on making #dftk algorithmically #differentiable at the #cecam workshop on #dft and #ai (https://www.cecam.org/workshop-details/1281). With his work derivatives of key density-functional theory quantities like forces or band structures wrt. model parameters can now be easily computed.

@schmitz (left) explaining his recent work on making #dftk algorithmically #differentiable at the #cecam workshop on #dft and #ai (https://www.cecam.org/workshop-details/1281). With his work derivatives of key density-functional theory quantities like forces or band structures wrt. model parameters can now be easily computed.

@schmitz (left) explaining his recent work on making #dftk algorithmically #differentiable at the #cecam workshop on #dft and #ai (https://www.cecam.org/workshop-details/1281). With his work derivatives of key density-functional theory quantities like forces or band structures wrt. model parameters can now be easily computed.