Combining materials design and deep learning: AI-enhanced luminescence thermometry with a novel Eu <sup>3+</sup> /Tb <sup>3+</sup> polymeric coordination compound
The study explores the effectiveness of various machine learning algorithms for luminescence thermometry. We demonstrate that the Convolutional Neural Network outperforms other machine learning algorithms and the ratiometric classical approach.
Authors
- Carlos D. S. Brites (ORCID: https://orcid.org/0000-0001-9636-2628)
- Trofim Polikovskiy (ORCID: https://orcid.org/0009-0001-2158-0986)
- Daria A. Metlina (ORCID: https://orcid.org/0000-0002-0047-9774)
- Yu. А. Belousov (ORCID: https://orcid.org/0000-0002-4976-4437)
- Ilya V. Taydakov (ORCID: https://orcid.org/0000-0001-9860-4141)
- Vladislav M. Korshunov (ORCID: https://orcid.org/0000-0003-3261-1140)
- Victoria E. Gontcharenko (ORCID: https://orcid.org/0000-0001-6290-8880)
- Mikhail T. Metlin (ORCID: https://orcid.org/0000-0003-3284-5019)
- Luís Carlos
- Sergey V Samsonov
Institutions
- National Research University Higher School of Economics (RU)
- P.N. Lebedev Physical Institute of the Russian Academy of Sciences (RU)
- Moscow Institute of Physics and Technology (RU)
- Bauman Moscow State Technical University (RU)
- Lomonosov Moscow State University (RU)
- University of Aveiro (PT)
Publication Details
- Journal
- Materials Chemistry Frontiers
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5qm00598a
- Citations
- 2
- Primary Topic
- Luminescence Properties of Advanced Materials
- Type
- article
- Field-Weighted Citation Impact
- 1.04