{"id":"doi:10.1007/s00521-021-06304-z","kind":"article","title":"Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques","authors":["Paulino José García Nieto","Esperanza García–Gonzalo","José P. Paredes–Sánchez"],"author_count":3,"year":2021,"published":"2021-07-20","venue":"Neural Computing and Applications","cited_by":66,"doi":"10.1007/s00521-021-06304-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s00521-021-06304-z","archive_url":"https://space-kitty.com/papers/doi:10.1007/s00521-021-06304-z","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3183906975","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06304-z.pdf","abstract":"Abstract This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques\" by Paulino José García Nieto, Esperanza García–Gonzalo, José P. Paredes–Sánchez, https://doi.org/10.1007/s00521-021-06304-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"superconductor-discovery","name":"Computational & AI-Driven Superconductor Discovery","domain_slug":"superconductivity"}],"cited_in_dossiers":[]}