Computational Design of a Tetrapericyclic Cycloaddition and the Nature of Potential Energy Surfaces with Multiple Bifurcations

Oil and gas pipelines are under great threat of corrosion due to the harsh service environment. It is critical to predict corrosion for the safe service of pipelines. Classical empirical-driven and mechanism-driven models have been successfully applied to predict the corrosion of oil and gas pipelines, while their complex applicability conditions and calculations become limitations. Datadriven models based machine learning (ML) are becoming the new trend owing to their efficiency and accuracy. This work systematically reviews these models including their evolution, characteristics, limitations, and performance, and highlights the application of data-driven models. In addition, a ML method database of corrosion prediction for oil and gas pipelines was created by summarizing the pre-processing, input and output parameters and performance metrics of ML models, which provide guidance for rational selection of models. Finally, conclusions and recommendations are presented and provide a broad outlook for future research paths.