CorroZoom: Active Learning Approach Towards Discovery of New Efficient Corrosion Inhibitors

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Active Learning Approach Towards Discovery of New Efficient Corrosion Inhibitors

Mikhail Zheludkevich
Institute of Surface Science
Helmholtz Zentrum Hereon
Geesthacht, Germany

Abstract
Organic corrosion inhibitors, whether added to protective coatings or directly to corrosive environments, play a crucial role in various active corrosion protection strategies. However, the quest for effective corrosion inhibitors within the vast chemical space remains a difficult challenge. Over the past decades, countless research papers have documented the inhibitory effects of individual compounds on various metals across diverse corrosive conditions, creating an infinite narrative. Fortunately, recent advancements in machine learning (ML) techniques offer promising avenues for narrowing the search and identifying potential candidates more efficiently. This work highlights the potential of computer-assisted methods in rapidly screening large numbers of organic compounds as potential corrosion inhibitors for magnesium and aluminum alloys. Our approach involves developing quantitative structure-property relationship (QSPR) models using ML algorithms, specifically support vector regression and kernel ridge regression. These models learn from existing data and generalize to predict the behavior of new compounds. To assess their robustness, we conducted experimental blind testing. The ML models leverage molecular descriptors derived from geometry and density functional theory calculations of organic compounds. Notably, two systematic approaches for sparse feature selection, identifying molecular descriptors most relevant to the corrosion inhibition efficiency of chemical compounds were proposed. This framework outperforms predictions based on randomly selected descriptors. To further enhance prediction quality, an active learning approach has been implemented. Experimental results from newly predicted modulators were incorporated into extended training data sets, iteratively improving model accuracy over time. In summary, ML-driven approaches hold great promise for accelerating the discovery of corrosion inhibitors. By harnessing computational tools, researchers can efficiently explore the vast chemical space and make informed decisions on the selection of corrosion inhibitors for specific applications.

Biography
Prof. Mikhail Zheludkevich currently serves as the Director of the Institute of Surface Science at Helmholtz-Zentrum Hereon, Germany. Additionally, he holds a full professorship at the University of Kiel. His research interests revolve around electrochemistry, multi-functional surfaces, and the active protection of lightweight materials and multi-material systems. In 2002, Prof. Zheludkevich earned his PhD in Physical Chemistry from the Belarusian State University, specializing in the interaction of atomic gases with metallic surfaces. Following a decade of diverse roles at the University of Aveiro in Portugal, ranging from post-doctoral researcher to group leader, he joined Helmholtz Zentrum Geesthacht as the head of the Corrosion and Surface Technology Department. In 2021, he established the Institute of Surface Science, a position he continues to lead. He has received recognition for his work, including the Tajima Prize from the International Society of Electrochemistry in 2016 and the distinction of Person of the Year 2023 by the International Mg Society. Furthermore, he serves as the chairman of the Aerospace Working Party at the European Federation of Corrosion.