C. Ozkan¹, L. Sahlmann², C. Feiler², M・ Zheludkevich², P. Taheri¹, J.M.C. Mol¹*

¹ Department of Materials Science and Engineering, Delft University of Technology,

Mekelweg 2, 2628 CD Delft, The Netherlands

² Institute of Surface Science, Helmholtz-Zentrum Hereon, Geesthacht, Germany

EXTENDED ABSTRACT: The use of chromates in corrosion protective coating technologies fbr structural aerospace applications is the subject of strict international health and safety regulations since many decades. High-throughput experimental setups and machine learning (ML) models show promise in discovering successor novel green inhibitors. However, a comprehensive quantitative electrochemical reference and standard fbr such approaches is still lacking. The literature often focuses on mechanistic explanations, and inhibitor performances are assessed with different electrochemical techniques at different times of exposure, different electrolytes and with different sample preparation procedures. This complicates combining and comparing the results from separate studies into one cohesive picture. In this study, various electrochemical techniques are used to illuminate and quantify the performance of potential inhibitors. The obtained experimental parameters can be employed directly as target parameter fbr training of a ML model that is predictive of the performance of untested compounds to create a shortlist of promising candidates. Moreover, the experimental investigation yields additional input features that can be combined with molecular descriptors derived from the molecular structure and atomistic simulations. These input features exhibit great potential to develop augmented quantitative structure­activity relationships as they allow to directly include information of the underlying mechanisms in training of the models. Keywords: corrosion inhibition; hexavalent chromium replacement; high-throughput screening; machine leamingReferences