Micro-scale potentiodynamic polarisation (log(j)) curves of 316L stainless steel

This database comprises 5 Potentiodynamic Polarisation (PP) datasets. Each dataset consists of a pair of CSVs: 1 file containing the values of the applied potential E (Vs Ag/AgCl); and 1 containing the corresponding log of the current density log(j) (µA/cm²) values. This database was deployed as the source dataset in the following scientific article, accepted for publication in npj Materials Degradation journal on 25 September 2023: "Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis". Leonardo Bertolucci Coelho1,2,∗, Daniel Torres1, Vincent Vangrunderbeek2, Miguel Bernal1, Gian Marco Paldino3, Gianluca Bontempi3, Jon Ustarroz 1,2 1 ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB), Brussels, Belgium 2 Research Group Electrochemical and Surface Engineering (SURF), Vrije Universiteit Brussel, Brussels, Belgium 3 Machine Learning Group (MLG), Université libre de Bruxelles (ULB), Brussels, Belgium *[e-mail] These datasets are almost identical to the ones available at https://data.mendeley.com/datasets/78rz8vw46x/2 The only difference is that eventual missing j values were filled with an iterative imputer (Python 3.7 language). The IterativeImputer class (from sklearn.impute) models each feature with missing values as a function of other features and uses that estimate for imputation.本数据库包含5组动电位极化（Potentiodynamic Polarisation, PP）数据集。每组数据集均包含一对CSV文件：其一存储施加电位E（相对于Ag/AgCl参比电极）的数值；其二存储对应的电流密度对数log(j)（单位：µA/cm²）数值。本数据库作为源数据集，被用于以下于2023年9月25日被npj Materials Degradation期刊接收待发表的学术论文：《基于机器学习与统计分析估算316L不锈钢的点蚀特征参数》(Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis)。作者信息如下：Leonardo Bertolucci Coelho1,2,∗, Daniel Torres1, Vincent Vangrunderbeek2, Miguel Bernal1, Gian Marco Paldino3, Gianluca Bontempi3, Jon Ustarroz 1,2。 1 布鲁塞尔自由大学（Université libre de Bruxelles, ULB）表面、界面与纳米材料化学研究室（ChemSIN），比利时布鲁塞尔 2 布鲁塞尔自由大学电化学与表面工程研究组（SURF），比利时布鲁塞尔 3 布鲁塞尔自由大学机器学习研究组（MLG），比利时布鲁塞尔 *通讯作者邮箱：[e-mail] 本数据集与公开链接https://data.mendeley.com/datasets/78rz8vw46x/2 中的数据集几乎完全一致，二者唯一区别为：本数据集使用基于Python 3.7开发的迭代插补器，对存在缺失的j值完成补全。sklearn.impute模块中的IterativeImputer类会将存在缺失值的特征建模为其余特征的函数，并基于该估算结果实现缺失值插补。