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Inverse design of core-shell particles with discrete material classes using neural networks

Kuhn, Lina 1; Repän, Taavi; Rockstuhl, Carsten 1,2
1 Institut für Theoretische Festkörperphysik (TFP), Karlsruher Institut für Technologie (KIT)
2 Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)


The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1–2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers.

Verlagsausgabe §
DOI: 10.5445/IR/1000152673
Veröffentlicht am 15.11.2022
DOI: 10.1038/s41598-022-21802-3
Zitationen: 4
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Institut für Theoretische Festkörperphysik (TFP)
Karlsruhe School of Optics & Photonics (KSOP)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2045-2322
KITopen-ID: 1000152673
HGF-Programm 43.32.02 (POF IV, LK 01) Designed Optical Materials
Erschienen in Scientific Reports
Verlag Nature Research
Band 12
Heft 1
Seiten Art.-Nr.: 19019
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 08.11.2022
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