Epit and Epass descriptors of 316L stainless steel estimated by Machine Learning

This database comprises 5 datasets of pitting/passivity descriptors estimated from Potentiodynamic Polarisation (PP) curves. Each dataset consists of 1 CSV file comprising the following features (columns): Epit_x, Epit_y, Epass_x, Epass_y. The “Maps” indexes (rows/data samples) correspond to the PP tests numbering. Epit_x and Epass_x, Epit_y and Epass_y correspond to Epit and Epass (V), log(jpit), and log(jpass) (µA/cm²). This descriptors database was derived from the 5 datasets of log(j) Vs E curves obtained in high throughput fashion with the SECCM on 316L stainless steel (5 different combinations of [NaCl] and scan rates). The descriptors datasets present the same amount of data samples as the source (log(j) Vs E) datasets (287, 377, 119, 125 and 47) available at: Bertolucci Coelho, Leonardo; Ustarroz, Jon (2023), “Micro-scale potentiodynamic polarisation (log(j)) curves of 316L stainless steel”, Mendeley Data, V1, doi: 10.17632/7j6b6y48jw.1 This descriptors database was deployed as described 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] In “Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis”, we provide a methodology for estimating Epass (passive potential) and Epit (pitting potential) from: 1. typical log(j) Vs E curves with a straightforward passivity breakdown (using an algorithm based on linear regression (LR)); 2. PP curves with more unique profiles mainly due to metastable events (using Artificial Neural Networks (ANN) trained on the LR estimates). For further details on the acquisition of the PP curves, please refer to: Bertolucci Coelho, Leonardo (2023), “Micro-scale potentiodynamic polarisation curves of 316L stainless steel ”, Mendeley Data, V3, doi: 10.17632/78rz8vw46x.3 Coelho, L. B. et al. Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution. Corros. Sci. 217, 111104 (2023).

本数据库包含5组由动电位极化（Potentiodynamic Polarisation, PP）曲线估算得到的点蚀/钝化特征参数集。每组数据集对应1个CSV文件，包含如下列特征：Epit_x、Epit_y、Epass_x、Epass_y。其中“映射”索引（行/数据样本）对应PP测试的编号。Epit_x与Epit_y、Epass_x与Epass_y分别对应点蚀电位（Epit）、log(jpit)与钝化电位（Epass）、log(jpass)，单位依次为V与µA/cm²。

本特征参数数据库源自5组以高通量方式通过扫描电化学池显微镜（Scanning Electrochemical Cell Microscopy, SECCM）获取的316L不锈钢log(j)-E曲线数据集，涵盖5种不同NaCl浓度与扫描速率的组合。该特征参数数据集与原始log(j)-E曲线数据集拥有相同的数据样本量，分别为287、377、119、125与47，原始数据集可在如下文献中获取：Bertolucci Coelho, Leonardo; Ustarroz, Jon (2023), "Micro-scale potentiodynamic polarisation (log(j)) curves of 316L stainless steel", Mendeley Data, V1, doi: 10.17632/7j6b6y48jw.1

本特征参数数据库的构建方法已在如下2023年9月25日被npj Materials Degradation期刊接收发表的科学文章中详述：《通过机器学习与统计分析估算316L不锈钢的点蚀特征参数》。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 *电子邮箱：[e-mail]

在《通过机器学习与统计分析估算316L不锈钢的点蚀特征参数》一文中，我们提出了一种从两类log(j)-E曲线中估算Epass（钝化电位）与Epit（点蚀电位）的方法：1. 具备典型钝化击穿特征的常规log(j)-E曲线（采用基于线性回归（Linear Regression, LR）的算法实现）；2. 因亚稳态事件呈现独特轮廓的PP曲线（采用基于LR估算结果训练得到的人工神经网络（Artificial Neural Networks, ANN）实现）。

如需进一步了解PP曲线的获取细节，请参阅如下文献：Bertolucci Coelho, Leonardo (2023), "Micro-scale potentiodynamic polarisation curves of 316L stainless steel ", Mendeley Data, V3, doi: 10.17632/78rz8vw46x.3；Coelho, L. B. et al. Probing the randomness of the local current distributions of 316L stainless steel corrosion in NaCl solution. Corros. Sci. 217, 111104 (2023).

搜集汇总

背景与挑战

背景概述

该数据集包含5个CSV文件，提供316L不锈钢的点蚀和钝化描述符（Epit和Epass），这些描述符通过机器学习方法从极化曲线中估计得出。数据来源于高通量SECCM实验，涉及不同氯化钠浓度和扫描速率组合，并用于支持相关科学文章中的分析方法。