diff --git a/_config.production.yml b/_config.production.yml index 2a9c2a1c..7a8b4207 100644 --- a/_config.production.yml +++ b/_config.production.yml @@ -162,6 +162,9 @@ foreverblog: true # Wormhole wormhole: true +# NotByAI +notbyAI: true + # Alive time alivetime: true alivestart: "08/08/2015" diff --git a/_config.tsukuba.yml b/_config.tsukuba.yml index f49b17aa..efd12515 100644 --- a/_config.tsukuba.yml +++ b/_config.tsukuba.yml @@ -158,6 +158,9 @@ foreverblog: true # Wormhole wormhole: true +# NotByAI +notbyAI: true + # Alive time alivetime: true alivestart: "08/08/2015" diff --git a/_config.yml b/_config.yml index 9f65cd5d..fd5a370f 100644 --- a/_config.yml +++ b/_config.yml @@ -158,6 +158,9 @@ foreverblog: true # Wormhole wormhole: true +# NotByAI +notbyAI: true + # Alive time alivetime: true alivestart: "08/08/2015" diff --git a/pages/en_index.md b/pages/en_index.md new file mode 100644 index 00000000..58765c01 --- /dev/null +++ b/pages/en_index.md @@ -0,0 +1,47 @@ +--- +layout: page +home-title: Welcome to zhonger's blog! +description: Writing, writing, writing ... +permalink: /en/index.html +langs: ["zh-Hans", "en"] +lang: "en" +--- + +# Introduction + +## 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 + +KVM +Docker +PHP +Python +Nodejs +Linux +R +Mysql +Photoshop + +## 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 + +- **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 + +Email: zhonger[at]live.cn (Please replace [at] with @.) diff --git a/pages/index.md b/pages/index.md index 8449f3e7..0d010686 100644 --- a/pages/index.md +++ b/pages/index.md @@ -3,45 +3,11 @@ layout: page home-title: Welcome to zhonger's blog! description: Writing, writing, writing ... permalink: / +langs: ["zh-Hans", "en"] +lang: "zh-Hans" --- -## 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 - -KVM -Docker -PHP -Python -Nodejs -Linux -R -Mysql -Photoshop - -## 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 - -- **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 - -Email: zhonger[at]live.cn (Please replace [at] with @.) - +# 个人简介 ## 关于我