Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning

Simple item page
dc.contributor.authorVallone, Alexen
dc.contributor.authorEstakhri, Nooshin M.en
dc.contributor.authorEstakhri, Nasim Mohammadien
dc.date.accessioned2023-09-22T17:08:26Zen
dc.date.available2023-09-22T17:08:26Zen
dc.date.issued2023-04en
dc.description.abstractMachine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced via random region specification in an inverse convolutional neural network. The trained model is used to design nanoparticles with high absorption levels and different ratios of absorption over scattering. The central design wavelength is shifted across 350-700 nm without re-training. We discuss the implications of wavelength, particle size, and the training dataset size on the performance of the model. Our approach may find interesting applications in the design of multilayer nanoparticles for biological, chemical, and optical applications as well as the design of low-scattering absorbers and antennas.en
dc.description.notesThis material is based upon work supported by the National Science Foundation under Grant No. 2138869 and the Chapman Faculty Opportunity Fund (2021).en
dc.description.sponsorshipNational Science Foundation [2138869]; Chapman Faculty Opportunity Fund (2021)en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1088/2515-7647/acc7e5en
dc.identifier.issn2515-7647en
dc.identifier.issue2en
dc.identifier.other24002en
dc.identifier.urihttp://hdl.handle.net/10919/116314en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherIOP Publishingen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmachine learningen
dc.subjectconvolutional neural networksen
dc.subjectnanoparticlesen
dc.subjectscatteringen
dc.subjectabsorptionen
dc.subjectinverse designen
dc.titleRegion-specified inverse design of absorption and scattering in nanoparticles by using machine learningen
dc.title.serialJournal of Physics-Photonicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Vallone_2023_J._Phys._Photonics_5_024002.pdf
Size:
1.14 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Scholarly Works, Physics