Elucidating Structure-Property Relationships in Aluminum Alloy Corrosion Inhibitors by Machine Learning
authors Galvao, TLP; Novell-Leruth, G; Kuznetsova, A; Tedim, J; Gomes, JRB
nationality International
journal JOURNAL OF PHYSICAL CHEMISTRY C
keywords MODELING CORROSION; DESCRIPTORS
abstract Organic corrosion inhibitors are playing a crucial role to substitute traditional protective technologies, which have acute toxicity problems associated. However, why some organic compounds inhibit corrosion and others do not is still not well understood. Therefore, we tested different machine learning (ML) methods to distinguish efficient corrosion inhibitors for aluminum alloys commonly used in aeronautical applications. In this work, we have obtained information that can greatly contribute to automate the search for new and more efficient protective solutions in the future: (i) a ML algorithm was selected that is able to classify correctly efficient inhibitors (i.e., with more than 50% efficiency) and non-inhibitors (i.e., with lower or equal to 50% efficiency), even when information about different alloys at different pHs is included in the same data set, which can significantly increase the information available to train the model; (ii) new descriptors related to the self-association of the molecules were evaluated, but improvements to the predictive power of the models are limited; (iii) average differences concerning the descriptors in this work were identified for inhibitors and non-inhibitors, having the potential to serve as guidelines to select potentially inhibitive molecular systems. This work demonstrates that ML can significantly accelerate research in the field by serving as a tool to perform an initial virtual screen of the molecules.
publisher AMER CHEMICAL SOC
issn 1932-7447
year published 2020
volume 124
issue 10
beginning page 5624
ending page 5635
digital object identifier (doi) 10.1021/acs.jpcc.9b09538
web of science category Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary
subject category Chemistry; Science & Technology - Other Topics; Materials Science
unique article identifier WOS:000526394800015
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journal impact factor 4.189
5 year journal impact factor 4.404
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