87 citations · 160 across the 5 of their papers we have counts for
5 papers
Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
Matthieu J. Verstraete, Joao Abreu, Guillaume E. Allemand +71
Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper p…
Machine learning Hubbard parameters with equivariant neural networks
Martin Uhrin, Austin Zadoks, Luca Binci +2
Density-functional theory with extended Hubbard functionals (DFT++) provides a robust framework to accurately describe complex materials containing transition-metal or rare-e…
Spectral Operator Representations
Austin Zadoks, Antimo Marrazzo, Nicola Marzari
Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic arrangements, typically decomposed into local atomic en…
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
Emanuele Bosoni, Louis Beal, Marnik Bercx +42
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter phys…
Common workflows for computing material properties using different quantum engines
Sebastiaan P. Huber, Emanuele Bosoni, Marnik Bercx +23
The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increa…