Superconductor discovery relies on theoretical guidelines for element selection
Modern studies confirm that superconductor discovery increasingly depends on rational theoretical guidelines, computational frameworks, and machine learning models for element selection and property prediction.
The retrieved literature consistently shows that discovering new superconductors moves away from purely trial-and-error approaches by implementing design concepts, physical guidelines, and machine-learning workflows to select promising elements and compositions.
B. Meredig, Erin Antono, Carena P Church, Maxwell Hutchinson, Julia Ling, S. Paradiso, B. Blaiszik, Ian T. Foster, Brenna M. Gibbons, J. Hattrick-Simpers, Ankita Mehta, Logan T. Ward. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. 2018. https://doi.org/10.1039/C8ME00012C
Paper [0] highlights that finding high-Tc superconductors relies heavily on advanced data sampling and targeted extrapolation techniques rather than random searching.
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Yoshikazu Mizuguchi. Discovery of BiS2-Based Superconductor and Material Design Concept. 2017. https://doi.org/10.3390/condmat2010006
Paper [5] details specific material design concepts and structural rules that led to the successful discovery of BiS2-based superconductors.
Md Tohidul Islam, Qinrui Liu, Scott R. Broderick. Machine Learning Accelerated Design of High-Temperature Ternary and Quaternary Nitride Superconductors. 2024. https://doi.org/10.3390/app14209196
Paper [6] demonstrates that machine-learning and computational design principles enable the targeted identification of high-temperature ternary and quaternary nitride superconductors.
Jung SG, Jung G, Cole JM. Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.. 2024. https://doi.org/10.1021/acs.jcim.4c01137
Paper [8] illustrates that systematic feature selection and composition-based modeling effectively guide the targeted discovery of novel superconductors and critical temperature predictions.
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