C. Song, T. Wiirger1, A. Lisitsyna, B. Vaghefinazari, M. L. Zheludkevich,
S. Albarqouni, C. Feiler, S.V. Lamaka
lnstitute of Surface Science, Helmholtz-Zentrum Hereon, Geesthacht, Germany,
Helmholtz AI, Helmholtz Center Munich, Neuherberg, Germany;
EXTENDED ABSTRACT: The latest developments in the field of experimental and computational screening of magnesium corrosion inhibitors will be presented. The overview will be given of high-throughput experimental methods [ 1 ], including recently developed techniques of image analysis and recognition. The pitfalls of this new approach have been uncovered by topographic and classical volume loss validation. New, larger than ever experimental database, composed of over 200 individual compounds, all tested at identical experimental conditions, has been collected. Three different approaches to quantification of inhibition efficiency will be compared in terms of linearity of the values of diverse datasets. The extensive experimental databases serve as input to train quantitative structure-property relationship models, employing machine learning and other AI algorithms. The models then predict effective corrosion inhibitors among hitherto untested potent commercially available compounds [2-4]. The importance of scientific data sharing will be highlighted. Following the best practices for uniform data reporting makes data sharing more efficient. A newly set corrosion inhibitor database incorporating over 2400 individual compounds for a variety of metallic substrates will be demonstrated.
Keywords: corrosion inhibition; high-throughput testing; in silico screening; magnesium
REFERENCES
[1] S.V. Lamaka, B. Vaghefinazari, D. Mei, R.P. Petrauskas, D. Roche, M.L. Zheludkevich. Corr. Sci. 128 (2017) 224
[2] C. Feiler, D. Mei, B. Vaghefinazari, T. Wilrger, R. H. MeiBner, B. Luthringer-Feyerabend, D. A. Winkler, M. L. Zheludkevich, S. V. Lamaka. Corr. Sci. (2020) 108245
[3] T. Wilrger, D. Mei, B. Vaghefinazari, D.A. Winkler, S.V. Lamaka, M.L. Zheludkevich, R.H. MeiBner, C. Feiler. npj Mater. Degrad. 5 (2021) 2
[4] T. Wiirger, L. Wang, D. Snihirova, M. Deng, S. Lamaka, D.A. Winkler, D. Roche, M. Zheludkevich, R.H. MeiBner, C. Feiler, J. Mater. Chem. A, 10 (2022) 21672
Dr. Sviatlana Lamaka is currently the Head of Department of "Electrochemistry and Big Data" at Institute of Surface Science of Helmholtz Zentrum Hereon in Germany. Her field of research is corrosion science, with emphasis on mechanistic understanding of light metal degradation in complex environments. Her research interests include high-throughput robotic and in silico screening of corrosion inhibitors, understanding their inhibition mechanisms and their compatibility with protective coatings. Dr. Lamaka co-authored over 150 peer-reviewed publications and patents, h = 50.