Update README.md

README.md

@@ -9,26 +9,11 @@
 
 🤝🤝🤝 Please star ⭐️ it for promoting open source projects 🌍 ! Thanks ! For inquiries or assistance, please don't hesitate to contact us at [email protected] (Dr. CAO Bin).
 
-**Bgolearn** has been implemented in the machine learning platform [MLMD](http://123.60.55.8/). 
+**Bgolearn** has been implemented in the machine learning platform [MLMD](http://123.60.55.8/). **Bgolearn Code** : [here](https://colab.research.google.com/drive/1OSc-phxm7QLOm8ceGJiIMGGz9riuwP6Q?usp=sharing) The **video of Bgolearn** has been uploaded to platforms : [BiliBili](https://www.bilibili.com/video/BV1Ae411J76z/?spm_id_from=333.999.0.0&vd_source=773e0c92141f498497cfafd0112fc146). [YouTube](https://www.youtube.com/watch?v=MSG6wcBol64&t=48s).
 
-**Bgolearn Code** : [here](https://colab.research.google.com/drive/1OSc-phxm7QLOm8ceGJiIMGGz9riuwP6Q?usp=sharing)
 
-The **video of Bgolearn** has been uploaded to platforms : [BiliBili](https://www.bilibili.com/video/BV1Ae411J76z/?spm_id_from=333.999.0.0&vd_source=773e0c92141f498497cfafd0112fc146). [YouTube](https://www.youtube.com/watch?v=MSG6wcBol64&t=48s).
-
-## cite
-1:
-    Cao, Bin and Su, Tianhao and Yu, Shuting and Li, Tianyuan and Zhang, Taolue and Dong, Ziqiang and Zhang, Tong-Yi, Active Learning Accelerates the Discovery of High Strength and High Ductility Lead-Free Solder Alloys. Available at SSRN: https://ssrn.com/abstract=4686075 or http://dx.doi.org/10.2139/ssrn.4686075. [GitHub : github.com/Bin-Cao/Bgolearn.]
-
-2:
-    Ma, J.∔, Cao, B.∔, Dong, S, Tian, Y, Wang, M, Xiong, J, Sun, S. et al. MLMD: a programming-free AI platform to predict and design materials. npj Comput Mater 10, 59 (2024). https://doi.org/10.1038/s41524-024-01243-4
-
-
-## links
 ![Screenshot 2023-11-16 at 11 23 35](https://github.com/Bin-Cao/Bgolearn/assets/86995074/cd0d24e4-06db-45f7-b6d6-12750fa8b819)
 
-- https://www.wheelodex.org/projects/bgolearn/
-- https://pypi.tuna.tsinghua.edu.cn/simple/bgolearn/
-- [user count](https://pypistats.org/packages/bgolearn)
 
 ### for regression
 - 1.Expected Improvement algorith (期望提升函数)
@@ -56,27 +41,23 @@ The **video of Bgolearn** has been uploaded to platforms : [BiliBili](https://ww
 
 - 3.Entropy-based approach (熵索函数)
 
-## Download History （- Nov16，2023）
-![WechatIMG4661](https://github.com/Bin-Cao/Bgolearn/assets/86995074/591e26b4-c8c3-4a17-ae8b-b3bcf9237514)
-
-
-if you have any questions or need help, you are welcome to contact me
-
-Source code: [![](https://img.shields.io/badge/PyPI-caobin-blue)](https://pypi.org/project/Bgolearn/)
-
 
 # Python package - Bgolearn 
 
 **No gradient** information is used
 ![plot](https://github.com/Bin-Cao/Bgolearn/assets/86995074/d4e43900-eadb-4ddf-af46-0208314de41a)
 
 
-## Package Document / 手册
-see 📒 [Bgolearn](https://bgolearn.netlify.app) (Click to view）
 
-见 📒 [中文说明](https://mp.weixin.qq.com/s/y-i_2ixbtJOv-nEYDu9THg) (Click to view）
+## Installing / 安装
+    pip install Bgolearn 
+
+## Checking / 查看
+    pip show Bgolearn 
+
+## Updating / 更新
+    pip install --upgrade Bgolearn
 
-Written using Python, which is suitable for operating systems, e.g., Windows/Linux/MAC OS etc.
 
 ## Template 
 ``` javascript
@@ -109,21 +90,6 @@ Mymodel = Bgolearn.fit(data_matrix = x, Measured_response = y, virtual_samples =
 Mymodel.EI()
 ```
 
-## Installing / 安装
-    pip install Bgolearn 
-
-## Checking / 查看
-    pip show Bgolearn 
-
-## Updating / 更新
-    pip install --upgrade Bgolearn
-
-
-## Update log / 日志
-Before version 2.0, function building
-
-Bgolearn V2.1.1 Jun 9, 2023. *para noise_std* By default, the built-in Gaussian process model estimates the noise of the input dataset by maximum likelihood, and yields in a more robust model.
-
 ## Multi-task design
     pip install BgoKit 
 
@@ -141,34 +107,14 @@ See : [Link](https://github.com/Bin-Cao/Bgolearn/blob/main/Template/%E4%B8%AD%E6
 <img src="https://github.com/Bin-Cao/Bgolearn/assets/86995074/41c90c29-364c-47cc-aefe-4433f7d93e23" alt="1" width="300" height="300">
 
 
-``` javascript
-Thank you for choosing Bgolearn for materials design. 
-Bgolearn is developed to facilitate the application of machine learning in research.
-Bgolearn is designed for optimizing single-target material properties.
-The BgoKit package is being developed to facilitate multi-task design.
-
-
-If you need to perform multi-target optimization, here are two important reminders:
-
-1. Multi-tasks can be converted into a single task using domain knowledge. 
-For example, you can use a weighted linear combination in the simplest situation. That is, y = w*y1 + y2...
 
-2. Multi-tasks can be optimized using Pareto fronts. 
-Bgolearn will return two arrays based on your dataset: 
-the first array is a evaluation score for each virtual sample, 
-while the second array is the recommended data considering only the current optimized target.
-
-The first array is crucial for multi-task optimization. 
-For instance, in a two-task optimization scenario, you can evaluate each candidate twice for the two separate targets. 
-Then, plot the score of target 1 for each sample on the x-axis and the score of target 2 on the y-axis. 
-The trade-off consideration is to select the data located in the front of the banana curve.
+## cite
+1:
+    Cao B., Su T, Yu S, Li T, Zhang T, Zhang J, Dong Z, Zhang Ty. Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys, Materials & Design, 2024, 112921, ISSN 0264-1275, https://doi.org/10.1016/j.matdes.2024.112921.
 
-I am delighted to invite you to participate in the development of Bgolearn. 
-If you have any issues or suggestions, please feel free to contact me at [email protected].
-```
+2:
+    Ma J.∔, Cao B.∔, Dong S, Tian Y, Wang M, Xiong J, Sun S. MLMD: a programming-free AI platform to predict and design materials. npj Comput Mater 10, 59 (2024). https://doi.org/10.1038/s41524-024-01243-4
 
-## References / 参考文献
-See : [papers](https://github.com/Bin-Cao/Bgolearn/tree/main/Refs)
 
 ## About / 更多
 Maintained by Bin Cao. Please feel free to open issues in the Github or contact Bin Cao