Update publications

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
 
 <img src="{{ site.baseurl }}/assets/icons/kvm.webp" alt="KVM" class="interest">
 <img src="{{ site.baseurl }}/assets/icons/docker.webp" alt="Docker" class="interest">
@@ -21,31 +21,33 @@ permalink: /
 <img src="{{ site.baseurl }}/assets/icons/mysql.png" alt="Mysql" class="interest">
 <img src="{{ site.baseurl }}/assets/icons/photoshop.svg" alt="Photoshop" class="interest">
 
-# 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
 
 &emsp;&emsp;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
 
 <img src="{{ site.baseurl }}/assets/icons/kvm.webp" alt="KVM" class="interest">
 <img src="{{ site.baseurl }}/assets/icons/docker.webp" alt="Docker" class="interest">
@@ -57,21 +59,23 @@ Email: zhonger[at]live.cn (Please replace [at] with @.)
 <img src="{{ site.baseurl }}/assets/icons/mysql.png" alt="Mysql" class="interest">
 <img src="{{ site.baseurl }}/assets/icons/photoshop.svg" alt="Photoshop" class="interest">
 
-# 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])
+邮箱：zhonger[at]live.cn (请使用@替换[at])