From 1434e78b90e2b8b29bd4fb9a15af7fdb8f4a11eb Mon Sep 17 00:00:00 2001 From: zhonger Date: Wed, 28 Jun 2023 12:50:53 +0900 Subject: [PATCH] Update publications --- pages/index.md | 46 +++++++++++++++++++++++++--------------------- 1 file changed, 25 insertions(+), 21 deletions(-) diff --git a/pages/index.md b/pages/index.md index d873e019..8449f3e7 100644 --- a/pages/index.md +++ b/pages/index.md @@ -5,11 +5,11 @@ description: Writing, writing, writing ... permalink: / --- -# About me +## About me   I'm Li Shengzhou. Nowadays, I am a PhD student of Computer Science in University of Tsukuba. My research topic is "Data-Driven and Machine Learning Based Material Science Research" under the supervision of Pro. Nakata Ayako from NIMS and Pro. Sakurai Tetsuya from University of Tsukuba. -# Interests +## Interests KVM Docker @@ -21,31 +21,33 @@ permalink: / Mysql Photoshop -# Educations +## Educations - Shanghai University (China), School of Computer Engineering and Science, Bachelor degree. (2012/09~2016/06) - Shanghai University (China), School of Computer Engineering and Science, Master degree. (2016/09~2019/04) - Northeast Normal University (China), Learning Japanese. (2019/10~2020/08) - University of Tsukuba (Japan), Graduate School of Science and Technology, Degree Programs in Systems and Information Engineering, Doctoral Program in Computer Science. (2020/10~Now) (MEXT Scholarship) -# Publications +## Publications -1. **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) -2. 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) -3. 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) -4. 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) -5. 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) +- **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) +- 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) -# Contact +## Contact Email: zhonger[at]live.cn (Please replace [at] with @.) -# 关于我 +## 关于我   我是李盛洲，目前我正在筑波大学攻读计算机博士学位。我的导师是NIMS的中田彩子研究员和筑波大学的樱井铁也教授，我的主要研究方向是《基于数据驱动和机器学习的材料科学研究》。 -# 研究兴趣 +## 研究兴趣 KVM Docker @@ -57,21 +59,23 @@ Email: zhonger[at]live.cn (Please replace [at] with @.) Mysql Photoshop -# 教育经历 +## 教育经历 - 上海大学（中国），计算机工程与科学学院，工学学士（2012年9月~2016年6月） - 上海大学（中国），计算机工程与科学学院，工学硕士（2016年9月~2019年4月） - 东北师范大学（中国），留日预备学校，日语学习（2019年10月~2020年8月） - 筑波大学（日本），情报工学部（计算机科学），博士在读（2020年10月~至今）（文部科学省奖学金） -# 论文发表 +## 论文发表 -1. **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) -2. 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) -3. 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) -4. 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) -5. 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) +- **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) +- 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) -# 联系我 +## 联系我 -邮箱：zhonger[at]live.cn (请使用@替换[at]) \ No newline at end of file +邮箱：zhonger[at]live.cn (请使用@替换[at])