diff --git a/README.md b/README.md index eb65318..f16fd9e 100644 --- a/README.md +++ b/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 bcao686@connect.hkust-gz.edu.cn (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) @@ -56,14 +41,6 @@ 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 @@ -71,12 +48,16 @@ Source code: [![](https://img.shields.io/badge/PyPI-caobin-blue)](https://pypi.o ![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 1 -``` 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 binjacobcao@gmail.com. -``` +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