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• Bgolearn : The Source Code of Bgolearn
• MultiBgolearn: Multi-Objective Bayesian Global Optimization for Materials Design
• BgoFace : User interface of the Bgolearn platform
• CodeDemo : Provides code demonstrations and data to illustrate the application of Bgolearn
• Document : The Document of Bgolearn
• MLMD : A programming-free AI platform to predict and design materials

• Cao, B., Su, T., Yu, S., Li, T., Zhang, T., Zhang, J., ... & Zhang, T. Y. (2024). Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys. Materials & Design, 241, 112921. https://doi.org/10.1016/j.matdes.2024.112921

Multi-target selection is not the same as multi-target optimization!

• Multi-target optimization considers multiple properties simultaneously in both prediction and utility space. It can be implemented using MultiBgolearn in Python.

• Multi-target selection typically considers two properties independently and then combines them in either the property space or utility space using a Pareto front. A method is constructed to select one solution from the points on the Pareto front. This can be implemented using BgoKit in Python.

Note: We are not claiming that one approach is better than the other; they are fundamentally different. Multi-target optimization truly accounts for the interdependencies between properties, whereas multi-target selection treats properties more independently.

If you are still unclear about the differences, please refer to the video for further explanation.