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The code combines the GBR algorithm, feature engineering and hyperparameter search for machine-learning-aided aterial discovery

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GBR_Regression

The code combines the GBR algorithm, feature engineering and hyperparameter search for machine-learning-aided aterial discovery.

References & citing

The theory behind GBR_Regression is described in our preprint (1), (2) and (3). If you use this repository in your work, please consider citing (1), (2) and (3):

  1. https://www.nature.com/articles/s41467-018-05761-w
  2. https://onlinelibrary.wiley.com/doi/abs/10.1002/smtd.201900360
  3. https://www.sciencedirect.com/science/article/pii/S2451929421005805

Authors

GBR_Regression is being developed by Shuaihua Lu under the guidance of Jinlan Wang at Southeast University.

Contact, questions, and contributing

If you have questions, please don't hesitate to reach out at [email protected].

If you find a bug or have a proposal for a feature, please post it in the Issues. If you have a question, topic, or issue that isn't obviously one of those, try our GitHub Discussions.

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The code combines the GBR algorithm, feature engineering and hyperparameter search for machine-learning-aided aterial discovery

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