Parabolic rate constants, k_p, were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between ~800 and 2000 K (~500–1700 ^oC; 900–3000 ^oF). Data were collected for environments including lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Materials studied include low- and high-Cr ferritic and austenitic steels, nickel superalloys, and aluminide materials. A combination of Arrhenius analysis, simple linear regression, supervised and unsupervised machine learning methods were used to investigate the relations between composition and oxidation kinetics. The supervised machine learning techniques produced the lowest mean standard errors. The most significant elements controlling oxidation kinetics were Ni, Cr, Al, and Fe, with Mo and Co composition also found to be significant features. The activation energies produced from the machine learning analysis were in the correct distributions for the diffusion constants for the oxide scales expected to dominate in each class.

抛物线速率常数k _p是从已发表的报告中收集的，并根据腐蚀产物数据（样品质量增加或腐蚀产物厚度）计算得出，并针对暴露于 ~800 至 2000 K（~500-1700  ^o C；900 –3000  ^oF）。收集的环境数据包括实验室空气、环境和超临界二氧化碳、超临界水和蒸汽。研究的材料包括低铬和高铬铁素体钢和奥氏体钢、镍高温合金和铝化物材料。Arrhenius 分析、简单线性回归、监督和非监督机器学习方法的组合被用来研究成分和氧化动力学之间的关系。监督机器学习技术产生最低的平均标准误差。控制氧化动力学的最重要元素是 Ni、Cr、Al 和 Fe，还发现 Mo 和 Co 成分是重要的特征。