Released DFTK version 0.7.22: https://dftk.org/releases with initial support for exact exchange and #HybridDFT and notable #performance engineering for #GPU-based #response calculations. Special thanks to Augustin Bussy (CSCS) and Tobias Schäfer (TU Wien) as well as all other #dftk contributors.
#densityfunctionaltheory #condensedmatter #dfpt #physics #simulation #planewave
Released #dftk version 0.7.19: https://dftk.org/releases with support for #hubbard corrections (#DFT+U) and various #gpu-related #performance improvements.
#densityfunctionaltheory #condensedmatter #dfpt #response #physics #simulation #planewave
New preprint: https://arxiv.org/abs/2509.07785
We present an implementation of AD-DFPT, a unification of #automaticdifferentiation with classical #dfpt response techniques for #densityfunctionaltheory (#dft). We demonstrate its use for #property predition, #uncertainty propagation, design of new #materials as well as the #machinelearning of new #dft models.
#condensedmatter #planewave #response #physics #simulation #computation
We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT framework derivatives of any DFT output quantity with respect to any input parameter (e.g. geometry, density functional or pseudopotential) can be computed accurately without deriving gradient expressions by hand. We implement AD-DFPT into the Density-Functional ToolKit (DFTK) and show its broad applicability. Amongst others we consider the inverse design of a semiconductor band gap, the learning of exchange-correlation functional parameters, or the propagation of DFT parameter uncertainties to relaxed structures. These examples demonstrate a number of promising research avenues opened by gradient-driven workflows in first-principles materials modeling.
Released #dftk version 0.7.14: https://dftk.org/releases with another round of #performance improvements for #nvidia GPUs as well as a faster algorithm for response calculations based on our recent #preprint http://arxiv.org/abs/2505.02319.
#densityfunctionaltheory #condensedmatter #dfpt #response #physics #simulation #planewave
New preprint from our team: https://arxiv.org/abs/2409.04372
A new algorithm for computing #material properties in #densityfunctionaltheory (#dft) based on inexact #krylov methods: we safe 40% computational cost by an adaptive selection of convergence tolerances inspired from #mathematical analysis.
#condensedmatter #planewave #dfpt #response #physics #simulation #computation
We use an exact Moreau-Yosida regularized formulation to obtain the exchange-correlation potential for periodic systems. We reveal a profound connection between rigorous mathematical principles and efficient numerical implementation, which marks the first computation of a Moreau-Yosida-based inversion for physical systems. We develop a mathematically rigorous inversion algorithm which is demonstrated for representative bulk materials, specifically bulk silicon, gallium arsenide, and potassium chloride. Our inversion algorithm allows the construction of rigorous error bounds that we are able to verify numerically. This unlocks a new pathway to analyze Kohn-Sham inversion methods, which we expect in turn to foster mathematical approaches for developing approximate functionals.
Michael Herbst
MatMat