From 735d5f752cf8cb648a76a9af6b56c483344257ed Mon Sep 17 00:00:00 2001 From: zhonger Date: Thu, 28 Mar 2024 17:13:24 +0900 Subject: [PATCH] Update publications --- pages/en_index.md | 7 ++++--- pages/index.md | 7 ++++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/pages/en_index.md b/pages/en_index.md index 58765c01..ce0ec999 100644 --- a/pages/en_index.md +++ b/pages/en_index.md @@ -35,9 +35,10 @@ lang: "en" ## Publications - **S Li**, H Zhang, D Dai, G Ding, X Wei, Y Guo. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. *Journal of Alloys and Compounds*, 2019, 782: 110-118.[[DOI]](https://doi.org/10.1016/j.jallcom.2018.12.136) -- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349) -- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html) -- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(Chinese) +- Wei X, Zhang Y, Liu X, J Peng, **S Li**, R Che, H Zhang. A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite $$A_2B^+B^{3+}X_6$$ [J]. *Journal of Materials Chemistry A*, 2023.[[DOI]](https://doi.org/10.1039/D3TA03600F) +- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys[J]. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349) +- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys[J]. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html) +- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. *The Chinese Journal of Nonferrous Metals*, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(Chinese) - Y Liu, H Zhang, Y Xu, **S Li**, D Dai, C Li, G Ding, W Shen, Q Qian. Prediction of Superconducting Transition Temperature Using A Machine-Learning Method[J]. *Materiali in tehnologije*, 2018, 52(5): 639-643.[[DOI]](https://doi.org/10.17222/mit.2018.043) - H Zhang, G Zhou, **S Li**, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. *Computational Materials Science*, 2020, 172: 109350.[[DOI]](https://doi.org/10.1016/j.commatsci.2019.109350) - D Dai, T Xu, H Hu, Z Guo, Q Liu, **S Li**, Q Qian, Y Xu, H Zhang. A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning[J]. *Available at SSRN 3442010*.[[DOI]](https://dx.doi.org/10.2139/ssrn.3442010) diff --git a/pages/index.md b/pages/index.md index 0d010686..5ed06343 100644 --- a/pages/index.md +++ b/pages/index.md @@ -35,9 +35,10 @@ lang: "zh-Hans" ## 论文发表 - **S Li**, H Zhang, D Dai, G Ding, X Wei, Y Guo. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. *Journal of Alloys and Compounds*, 2019, 782: 110-118.[[DOI]](https://doi.org/10.1016/j.jallcom.2018.12.136) -- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349) -- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html) -- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(中文) +- Wei X, Zhang Y, Liu X, J Peng, **S Li**, R Che, H Zhang. A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite $$A_2B^+B^{3+}X_6$$ [J]. *Journal of Materials Chemistry A*, 2023.[[DOI]](https://doi.org/10.1039/D3TA03600F) +- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys[J]. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349) +- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys[J]. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html) +- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. *The Chinese Journal of Nonferrous Metals*, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(中文) - Y Liu, H Zhang, Y Xu, **S Li**, D Dai, C Li, G Ding, W Shen, Q Qian. Prediction of Superconducting Transition Temperature Using A Machine-Learning Method[J]. *Materiali in tehnologije*, 2018, 52(5): 639-643.[[DOI]](https://doi.org/10.17222/mit.2018.043) - H Zhang, G Zhou, **S Li**, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. *Computational Materials Science*, 2020, 172: 109350.[[DOI]](https://doi.org/10.1016/j.commatsci.2019.109350) - D Dai, T Xu, H Hu, Z Guo, Q Liu, **S Li**, Q Qian, Y Xu, H Zhang. A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning[J]. *Available at SSRN 3442010*.[[DOI]](https://dx.doi.org/10.2139/ssrn.3442010)