TY - JOUR
T1 - Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors
AU - Winkler, David A.
AU - Hughes, Anthony E.
AU - Özkan, Can
AU - Mol, Arjan
AU - Würger, Tim
AU - Feiler, Christian
AU - Zhang, Dawei
AU - Lamaka, Sviatlana V.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Corrosion inhibitor mechanisms
KW - Corrosion inhibitors
KW - High-throughput corrosion inhibition testing
KW - Machine learning
KW - Molecular design
KW - Organic molecules
UR - http://www.scopus.com/inward/record.url?scp=85208117208&partnerID=8YFLogxK
U2 - 10.1016/j.pmatsci.2024.101392
DO - 10.1016/j.pmatsci.2024.101392
M3 - Review article
AN - SCOPUS:85208117208
SN - 0079-6425
VL - 149
JO - Progress in Materials Science
JF - Progress in Materials Science
M1 - 101392
ER -