Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering.
CatBoost
Fourth paradigms
Jabir
Machine learning
Soraya
Superconductor
Transition temperature
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 Feb 2024
17 Feb 2024
Historique:
received:
07
10
2023
accepted:
13
02
2024
medline:
18
2
2024
pubmed:
18
2
2024
entrez:
17
2
2024
Statut:
epublish
Résumé
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (T
Identifiants
pubmed: 38368476
doi: 10.1038/s41598-024-54440-y
pii: 10.1038/s41598-024-54440-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3965Informations de copyright
© 2024. The Author(s).
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