activity
20212026
most citedMachine learning the derivative discontinuity of density-functional theory

35 citations · 63 across the 5 of their papers we have counts for

collaborators

5 papers

physics.optics2026

Spectrally smooth broadband response via autocorrelation-constrained inverse design

Johannes Gedeon, Rasmus Ellebæk Christiansen, Ole Sigmund

Time-domain inverse design in photonics is known to be suitable for maximizing the efficiency of optical devices over broad frequency ranges. Objectives commonly used in this conte…

physics.optics2025

Topology optimization of a superabsorbing thin-film semiconductor metasurface

Johannes Gedeon, Izzatjon Allayarov, Emadeldeen Hassan +1

We demonstrate a computational inverse design method for optimizing broadband-absorbing metasurfaces made of arbitrary dispersive media. Our figure of merit is the time-averaged in…

physics.optics2024★ 6 cited

Time-domain topology optimization of power dissipation in dispersive dielectric and plasmonic nanostructures

Johannes Gedeon, Izzatjon Allayarov, Antonio Calà Lesina +1

We present a density-based topology optimization scheme for locally optimizing the electric power dissipation in nanostructures made of lossy dispersive materials. By using the com…

physics.optics2023★ 22 cited

Free-form inverse design of arbitrary dispersive materials in nanophotonics

Johannes Gedeon, Emadeldeen Hassan, Antonio Calà Lesina

In the last decades nanostructures have unlocked myriads of functionalities in nanophotonics by engineering light-matter interaction beyond what is possible with conventional bulk…

physics.chem-ph2021★ 35 cited

Machine learning the derivative discontinuity of density-functional theory

Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson +3

Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned func…