Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.

ABSTRACT: In the quest for advanced superconducting materials, the accurate prediction of critical temperatures (T_c) poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and structural characteristics of a given material. To address this challenges, we have developed a machine-learning framework that aims to elucidate these complicated and hitherto poorly understood structure-property and property-property relationships. This study introduces a novel machine-learning-based workflow, termed the Gradient Boosted Feature Selection (GBFS), which has been tailored to predict T_c for superconductors by employing a distributed gradient-boosting framework. This approach integrates exploratory data an

SUBMITTER: Jung SG 

PROVIDER: S-EPMC11481088 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature