Despite the remarkable success of machine learning in materials science, challenges persist in gaining mechanistic insights, especially in low-data regimes where dataset sizes limit the precise applicability of machine learning. The prevailing reliance on high-confidence predictions from the models often leaves the underlying decision-making mechanisms opaque, limiting scientific understanding. This study presents an alternative approach that emphasizes understanding the model decision-making process over individual predictions, enabling the extraction of scientifically meaningful insights from small datasets. Focusing on 107 small organic molecules and their corrosion inhibition properties as a case study, we systematically evaluate 29 molecular featurization methods and 9 target representations, generating over 12 thousand model configurations to identify robust feature-target pairings. We reveal common trends by reverse engineering the best-performing models based on featurization methods of physicochemical descriptors, hashed fingerprints, and structural keys, which we integrate with domain knowledge to create a molecular substructure template for candidate molecules. Using this template, we filter a toxicity database to identify non-toxic corrosion inhibitors, aiming to replace the de facto but hazardous corrosion inhibitor hexavalent chromium. The resulting candidate’s efficacy is validated through electrochemical testing, illustrating the feasibility of achieving mechanistic insights from statistical models in data-scarce environments.

The targeted removal of efficient but toxic corrosion inhibitors based on hexavalent chromium has provided an impetus for discovery of new, more benign organic compounds to fill that role. Developments in high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, the increased capabilities of machine learning (ML) methods, and a better understanding of mechanisms of inhibition provide the potential to make discovery of new corrosion inhibitors faster and cheaper than ever before. These technical developments in the corrosion inhibition field are summarized herein. We describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. The literature on inhibition mechanisms is briefly summarized along with quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these methods provides a paradigm for the rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys, in diverse environments. A comprehensive list of corrosion inhibitors tested for various substrates that was curated as part of this review is accessible online https://excorr.web.app/database and available in a machine-readable format.

Integrated attenuated total reflection – Fourier transform infrared spectroscopy (ATR-FTIR) – Electrochemical impedance spectroscopy (EIS) measurements were used to simultaneously follow chemisorption mechanisms of organic inhibitors as well as their corrosion inhibition efficiency towards magnesium based substrates. Four carboxylic compounds, i.e. 2,5-pyridinedicarboxylic acid (PDC), 3-methylsalicylic acid (MSA), sodium salicylate (SS) and fumaric acid (FA), were selected based on their promising inhibiting capacities and were all shown to chemisorb at the MgO/Mg(OH)₂ surface by carboxylate bond formation. Orientation analysis using polarized infrared light showed that carboxylate bonds established using aliphatic carboxylate compound aligned perpendicular to the magnesium surface, whereas carboxylate bonds with aromatic compounds were oriented in plane with the magnesium surface. This different orientation is associated to the involvement of π-interactions in the MgO/Mg(OH)₂ – aromatic carboxylate adsorption. Additionally, DFT calculations revealed that the addition of hetero-atoms (i.e. N or OH) in the molecular structure contributes to increased adsorption energies, indicating that next to carboxylate groups also these hetero-atoms are involved in interfacial interactions. Integrating the ATR-FTIR setup with an electrochemical cell allowing for simultaneous EIS measurements lead to two surface phenomena determining the inhibition efficiency. Surface hydroxylation processes on one hand forming a MgO/Mg(OH)₂ layer on one hand, and the chemisorption of carboxylate compounds on the other hand. The inhibition efficiency was found to increase in following order: FA < PDC < MSA and was mainly associated to the formation of a MgO/Mg(OH)₂ layer. SS was shown to act as a corrosion accelerator rather than a corrosion inhibitor. Despite its high sensitivity for water, both surface processes could be followed in situ by means of ATR-FTIR. Simultaneously, protective properties of the formed films could be quantified by means of EIS. Consequently, integrated ATR-FTIR – EIS methodology has shown to be highly valuable for gaining in-situ insights in the inhibition mechanism, while quantifying the inhibition efficiency. This was even possible for highly active metal substrate as magnesium, although further developments are suggested if one aims to quantify electrochemical constants related to corrosion and other surface processes measured at the low frequencies (i.e. < 1 Hz).