Fig. 2: Schematic representation of the ML workflow used in this study.
From: Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques
A database of 58 small organic molecules and their corrosion responses on AZ91 are employed as training database. First a pool of molecular descriptors to encode their molecular structure is generated and exposed to a two-step sparse feature selection approach. The most relevant descriptors are subsequently used to train supervised machine learning models to predict the behavior of untested chemicals. The small organic molecules for this step are selected following our previously published ExChem21 approach.
