Superconductor discovery relies on theoretical guidelines for element selection
the verdict
SUPPORTED
the evidence backs this
confidence 89/100
Modern studies confirm that superconductor discovery increasingly depends on rational theoretical guidelines, computational frameworks, and machine learning models for element selection and property prediction.
Evidence for · 4
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
2018 · cited by 187
Paper [0] highlights that finding high-Tc superconductors relies heavily on advanced data sampling and targeted extrapolation techniques rather than random searching.
See more details
More for · 3
Discovery of BiS2-Based Superconductor and Material Design Concept
2017 · cited by 10
Paper [5] details specific material design concepts and structural rules that led to the successful discovery of BiS2-based superconductors.
Machine Learning Accelerated Design of High-Temperature Ternary and Quaternary Nitride Superconductors
2024 · cited by 6
Paper [6] demonstrates that machine-learning and computational design principles enable the targeted identification of high-temperature ternary and quaternary nitride superconductors.
Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.
2024 · cited by 4
Paper [8] illustrates that systematic feature selection and composition-based modeling effectively guide the targeted discovery of novel superconductors and critical temperature predictions.