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Journal Article

Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys

MPS-Authors
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Sasidhar,  Kasturi Narasimha
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Hamidi Siboni,  Nima
Ab Initio Thermodynamics, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
DeepMetis, Lohmühlenstraße 65, 12435 Berlin, Germany;

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Mianroodi,  Jaber Rezaei
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Ergodic Labs, Lohmühlenstraße 65, 12435, Berlin, Germany;

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Rohwerder,  Michael
Corrosion, Interface Chemistry and Surface Engineering, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Raabe,  Dierk
Sustainable Synthesis of Materials, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Sasidhar, K. N., Hamidi Siboni, N., Mianroodi, J. R., Rohwerder, M., Neugebauer, J., & Raabe, D. (2022). Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys. npj Materials Degradation, 6(1): 71. doi:10.1038/s41529-022-00281-x.


Cite as: https://hdl.handle.net/21.11116/0000-000A-E9CB-B
Abstract
We have developed a deep-learning-based framework for understanding the individual and mutually combined contributions of different alloying elements and environmental conditions towards the pitting resistance of corrosion-resistant alloys. A fully connected deep neural network (DNN) was trained on previously published datasets on corrosion-relevant electrochemical metrics, to predict the pitting potential of an alloy, given the chemical composition and environmental conditions. Mean absolute error of 170 mV in the predicted pitting potential, with an R-square coefficient of 0.61 was obtained after training. The trained DNN model was used for multi-dimensional gradient descent optimization to search for conditions maximizing the pitting potential. Among environmental variables, chloride-ion concentration was universally found to be detrimental. Increasing the amounts of dissolved nitrogen/carbon was found to have the strongest beneficial influence in many alloys. Supersaturating transition metal high entropy alloys with large amounts of interstitial nitrogen/carbon has emerged as a possible direction for corrosion-resistant alloy design.