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RESEARCH PAPER · Materiali in tehnologije · 2018

Prediction of superconducting transition temperature using a machine-learning method

Yuan-Peng LIU, H. Zhang, Yuge Xu, Shengzhou Li, D. Dai, C. Li, G. Ding, W. Shen, Quan Qian

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10 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Prediction of superconducting transition temperature using a machine-learning methodOriginalPeaks in the electronic density of states are not strong predictors of Superconductivity0Comparative Performance Analysis of Neural Network Base Training Algorithm and Neuro-Fuzzy System with SOM for the Purpose of Prediction of the Features of Superconductors12Machine learning in materials design: Algorithm and application*62Testing whether flat bands in the calculated electronic density of states are good predictors of superconducting materials10Application of machine learning for advanced material prediction and design123Upper limit of the transition temperature of superconducting materials5Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression26Machine Learning Algorithms, Tools, and Databases for Applications in Materials Science0Enhanced permittivity prediction in microwave dielectric ceramics and structure-property insights via CGCNN combined with GNNExplainer2Predicting the critical temperature of superconductors and materials classification with a balanced dataset without prior knowledge2

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  1. Peaks in the electronic density of states are not strong predictors of Superconductivity

    arXiv (Cornell University) · 2020-07-15

    Zoë S. Yang, Austin M. Ferrenti, R. J. Cava

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  2. Comparative Performance Analysis of Neural Network Base Training Algorithm and Neuro-Fuzzy System with SOM for the Purpose of Prediction of the Features of Superconductors

    Advances in intelligent systems and computing · 2020-08-14

    Subrato Bharati, Mohammad Atikur Rahman, Prajoy Podder, Md. Robiul Alam Robel, Niketa Gandhi

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  3. Machine learning in materials design: Algorithm and application*

    Chinese Physics B · 2020-10-14

    Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin

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  4. Testing whether flat bands in the calculated electronic density of states are good predictors of superconducting materials

    Journal of Physics and Chemistry of Solids · 2020-12-20

    Zoë S. Yang, Austin M. Ferrenti, R. J. Cava

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  5. Application of machine learning for advanced material prediction and design

    EcoMat · 2022-03-07

    Cheuk Hei Chan, Mingzi Sun, Bolong Huang

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  6. Upper limit of the transition temperature of superconducting materials

    Patterns · 2022-10-24

    Yang Liu, Haiyou Huang, Jie Yuan, Yan Zhang, Hongyuan Feng, Ning Chen, Yang Li, Jiao Teng, Kui Jin, Dezhen Xue, Yanjing Su

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  7. Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression

    Alexandria Engineering Journal · 2023-11-28

    Paulino José García Nieto, Esperanza García–Gonzalo, Luis Alfonso Menéndez-García, Laura Álvarez de Prado, Antonio Bernardo-Sánchez

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  8. Machine Learning Algorithms, Tools, and Databases for Applications in Materials Science

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Nilima R. Das, Swayam Aryam Behera, P. Kali Krishna, Tripti Sharma, Patnala Ganga Raju Achary

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  9. Enhanced permittivity prediction in microwave dielectric ceramics and structure-property insights via CGCNN combined with GNNExplainer

    Materials Today Chemistry · 2025-10-01

    Xiao-Bin Liu, Ning Shao, Qiuxia Huang, Lei Zhang, Ziheng Gao, Yapeng Tian, Haiquan Wang, Huanfu Zhou

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  10. Predicting the critical temperature of superconductors and materials classification with a balanced dataset without prior knowledge

    Journal of Applied Physics · 2026-01-13

    P. A. H. Nawoda, Gayan Prasad Hettiarachchi, M. D.T. Attygalle

    Read original paper ↗ ·
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