Publication: AI-based metamaterial design
dc.contributor.department | Department of Mechanical Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.department | KUTTAM (Koç University Research Center for Translational Medicine) | |
dc.contributor.department | School of Medicine | |
dc.contributor.kuauthor | Ahmadpour, Abdollah | |
dc.contributor.kuauthor | Tezsezen, Ece | |
dc.contributor.kuauthor | Yığcı, Defne | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.contributor.schoolcollegeinstitute | Research Center | |
dc.contributor.schoolcollegeinstitute | SCHOOL OF MEDICINE | |
dc.date.accessioned | 2024-12-29T09:36:03Z | |
dc.date.issued | 2024 | |
dc.description.abstract | The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.indexedby | PubMed | |
dc.description.issue | 23 | |
dc.description.openaccess | hybrid | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | TÜBİTAK | |
dc.description.sponsorship | S.T. acknowledges Tubitak 2232 International Fellowship for Outstanding Researchers Award (118C391), Alexander von Humboldt Research Fellowship for Experienced Researchers, Marie Sk & lstrok;odowska-Curie Individual Fellowship (101003361), and Royal Academy Newton- Katip Celebi Transforming Systems Through Partnership award for financial support of this research. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the TUBITAK. This work was partially supported by the Science Academy's Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBIP), and the Bilim Kahramanlari Dernegi Young Scientist Award. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. | |
dc.description.volume | 16 | |
dc.identifier.doi | 10.1021/acsami.4c04486 | |
dc.identifier.eissn | 1944-8252 | |
dc.identifier.issn | 1944-8244 | |
dc.identifier.quartile | Q1 | |
dc.identifier.scopus | 2-s2.0-85195049380 | |
dc.identifier.uri | https://doi.org/10.1021/acsami.4c04486 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/21921 | |
dc.identifier.wos | 1235247500001 | |
dc.keywords | Artificial intelligence (AI) | |
dc.keywords | Metamaterials | |
dc.keywords | Biomedical diagnostics | |
dc.keywords | Point-of-care | |
dc.keywords | Wearablesensors | |
dc.keywords | Optics | |
dc.keywords | Acoustics | |
dc.language.iso | eng | |
dc.publisher | American Chemical Society | |
dc.relation.ispartof | ACS APPLIED MATERIALS & INTERFACES | |
dc.subject | Nanoscience and nanotechnology | |
dc.subject | Materials science | |
dc.title | AI-based metamaterial design | |
dc.type | Review | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Tezsezen, Ece | |
local.contributor.kuauthor | Yığcı, Defne | |
local.contributor.kuauthor | Ahmadpour, Abdollah | |
local.contributor.kuauthor | Taşoglu, Savaş | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | SCHOOL OF MEDICINE | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit1 | Research Center | |
local.publication.orgunit2 | Department of Mechanical Engineering | |
local.publication.orgunit2 | KUTTAM (Koç University Research Center for Translational Medicine) | |
local.publication.orgunit2 | School of Medicine | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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