# Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Article · Paulino José García Nieto, Esperanza García–Gonzalo, José P. Paredes–Sánchez

- Archive id: doi:10.1007/s00521-021-06304-z
- Published: 2021-07-20
- Venue: Neural Computing and Applications
- DOI: https://doi.org/10.1007/s00521-021-06304-z
- Citations: 66
- Source URL: https://doi.org/10.1007/s00521-021-06304-z
- PDF: https://link.springer.com/content/pdf/10.1007/s00521-021-06304-z.pdf
- Provenance: openalex

## 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 reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Computational & AI-Driven Superconductor Discovery](https://space-kitty.com/topics/superconductor-discovery)

---
Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). Metadata: arXiv (CC0), OpenAlex (CC0), Europe PMC (CC BY/CC0 works), ClinicalTrials.gov.