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Record W4391168152 · doi:10.1515/nanoph-2023-0606

Can photonic heterostructures provably outperform single‐material geometries?

2024· article· en· W4391168152 on OpenAlexafffund
Alessio Amaolo, Pengning Chao, Thomas J. Maldonado, Sean Molesky, Alejandro W. Rodríguez

Bibliographic record

VenueNanophotonics · 2024
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsPolytechnique Montréal
FundersDefense Sciences Office, DARPADivision of Materials ResearchMaterials Research Science and Engineering Center, Harvard UniversityInstitut de Valorisation des DonnéesCornell Center for Materials ResearchDefense Advanced Research Projects AgencyDivision of Emerging FrontiersPrinceton UniversityDivision of Emerging Frontiers in Research and InnovationAdvanced Research Projects AgencyNational Science Foundation
KeywordsPhotonicsHeterojunctionComputer scienceInversePhotonic crystalRange (aeronautics)Absorption (acoustics)FabricationMaterials scienceNanotechnologyTopology (electrical circuits)Electronic engineeringOptoelectronicsMathematicsEngineeringElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Recent advances in photonic optimization have enabled calculation of performance bounds for a wide range of electromagnetic objectives, albeit restricted to single-material systems. Motivated by growing theoretical interest and fabrication advances, we present a framework to bound the performance of photonic heterostructures and apply it to investigate maximum absorption characteristics of multilayer films and compact, free-form multi-material scatterers. Limits predict trends seen in topology-optimized geometries - often coming within factors of two of specific designs - and may be utilized in conjunction with inverse designs to predict when heterostructures are expected to outperform their optimal single-material counterparts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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