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20212025
most citedHow to verify the precision of density-functional-theory implementations via reproducible and universal workflows

87 citations · 160 across the 5 of their papers we have counts for

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2 papers · 1 filter

cond-mat.mtrl-sci2024

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…

cond-mat.mtrl-sci2024★ 3 cited

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…