Predicting new superconductors and their perilous temperatures using computational intelligence.

Autor:	Sasikala, C., Prabakaran, S., Ragavendiran, S. D. Prabu, Gomathi, V., Revathy, G.
Předmět:	MACHINE learning SUPERCONDUCTORS SUPERCONDUCTIVITY CRITICAL temperature CONSTRUCTION materials COMPUTATIONAL intelligence IRON-based superconductors
Zdroj:	AIP Conference Proceedings; 2023, Vol. 2782 Issue 1, p1-4, 4p
Abstrakt:	Superconductivity has spurred a massive amount of research since its discovery more than a century ago. Several facets of this remarkable phenomenon, the most important of which is the link between superconductivity and chemical and structural features of materials, are yet unknown. To bridge the gap, a variety of machine learning algorithms are being developed. The machine learning algorithms are compared to yield the best ever result for the superconductivity of the materials. With NB and RF machine learning algorithms the critical temperatures is computed and it is 90% more accurate than other methodologies. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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