Update README.md

README.md

@@ -121,7 +121,10 @@ Bgolearn V2.1.1 Jun 9, 2023. *para noise_std* By default, the built-in Gaussian
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 Thank you for choosing Bgolearn for material design. 
 Bgolearn is developed to facilitate the application of machine learning in research.
-Bgolearn is designed for optimizing single-target material properties. 
+Bgolearn is designed for optimizing single-target material properties.
+The BgoKit package is being developed to facilitate multi-task design.
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 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. 
@@ -132,6 +135,14 @@ 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.
 
+from BgoKit import ToolKit
+Model = ToolKit.MultiOpt(vs,[score_1,score_2])
+Model.BiSearch()
+Model.plot_distribution()
+See : [Link]([https://github.com/Bin-Cao/Bgolearn/tree/main/Refs](https://github.com/Bin-Cao/Bgolearn/blob/main/Template/%E4%B8%AD%E6%96%87%E7%A4%BA%E4%BE%8B/%E5%A4%9A%E7%9B%AE%E6%A0%87%E5%AE%9E%E7%8E%B0/%E5%A4%9A%E7%9B%AE%E6%A0%87.ipynb))
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+
+
 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.