Machine learning for corrosion database

This database was firstly created for the scientific article entitled: "Reviewing Machine Learning of corrosion prediction: a data-oriented perspective" L.B. Coelho 1 , D. Zhang 2 , Y.V. Ingelgem 1 , D. Steckelmacher 3 , A. Nowé 3 , H.A. Terryn 1 1 Department of Materials and Chemistry, Research Group Electrochemical and Surface Engineering, Vrije Universiteit Brussel, Brussels, Belgium 2 A Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China 3 VUB Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium Different metrics are possible to evaluate the prediction accuracy of regression models. However, only papers providing relative metrics (MAPE, R²) were included in this database. We tried as much as possible to include descriptors of all major ML procedure steps, including data collection (“Data acquisition”), data cleaning feature engineering (“Feature reduction”), model validation (“Train-Test split”*), etc. *the total dataset is typically split into training sets and testing (unknown data) sets for performance evaluation of the model. Nonetheless, sometimes only the training or the testing performances were reported (“?” marks were added in the respective evaluation metric field(s)). The “Average R²” was sometimes considered for studies employing “CV” (cross-validation) on the dataset. For a detailed description of the ML basic procedures, the reader could refer to the References topic in the Review article.

本数据库最初为题为《腐蚀预测的机器学习研究综述：数据驱动视角》（Reviewing Machine Learning of corrosion prediction: a data-oriented perspective）的学术论文创建，作者包括L.B. Coelho¹、D. Zhang²、Y.V. Ingelgem¹、D. Steckelmacher³、A. Nowé³、H.A. Terryn¹。¹ 比利时布鲁塞尔自由大学材料与化学系电化学与表面工程研究组，布鲁塞尔；² 北京科技大学先进材料与技术研究所、国家材料腐蚀与防护数据中心、北京材料基因组工程高精尖创新中心，北京；³ 布鲁塞尔自由大学人工智能实验室，布鲁塞尔。

评估回归模型的预测精度可采用多种指标，但本数据库仅收录采用相对指标（平均绝对百分比误差（MAPE）、决定系数（R²））的相关研究。我们尽可能覆盖机器学习全流程关键步骤的描述字段，包括数据采集（"Data acquisition"）、数据清洗与特征工程（"Feature reduction"）、模型验证（"Train-Test split"*）等。*为完成模型性能评估，完整数据集通常会划分为训练集与测试集（即未知数据集合）。但部分研究仅报告了训练集或测试集的性能指标，此时对应评估指标字段将标注"？"。对于在数据集上采用交叉验证（cross-validation，CV）的研究，有时会采用"平均R²"作为评估指标。若需了解机器学习基础流程的详细说明，读者可参阅该综述论文的"参考文献"章节。