ArticleiScience2023
Machine learning enabled rational design for dynamic thermal emitters with phase change materials.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Design strategies, manufacturing, and applications of radiative cooling technologies.Nanophotonics (Berlin, Germany) · 2025Review
- Scalable VONanophotonics (Berlin, Germany) · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Dynamic thermal emitters have attracted considerable attention due to their potential in widespread applications such as radiative cooling, thermal switching, and adaptive camouflage. However, the state-of-art performances of dynamic emitters are still far below expectations. Here, customized to the special and stringent requirement of dynamic emitters, a neural network model is developed to effectively bridge the structural and spectral spaces and further realizes the inverse design with coupling to genetic algorithms, which considers the broadband spectral responses in different phase-states and utilizes comprehensive measures to ensure the modeling accuracy and computational speed. Besides achieving an outstanding emittance tunability of 0.8, the physics and empirical rules have also been mined qualitatively through decision trees and gradient analyses. The study demonstrates the feasibility of using machine learning to obtain the near-perfect performance of dynamic emitters, as well as guiding the design of other thermal and photonic nanostructures with multifunctions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.