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

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.

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 Creators:
Sasidhar, Kasturi Narasimha1, Author           
Hamidi Siboni, Nima2, 3, Author           
Mianroodi, Jaber Rezaei4, 5, 6, Author           
Rohwerder, Michael7, Author           
Neugebauer, Jörg8, Author           
Raabe, Dierk1, 9, Author           
Affiliations:
1Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
2Ab Initio Thermodynamics, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863338              
3DeepMetis, Lohmühlenstraße 65, 12435 Berlin, Germany, ou_persistent22              
4Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3243050              
5Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863392              
6Ergodic Labs, Lohmühlenstraße 65, 12435, Berlin, Germany, ou_persistent22              
7Corrosion, Interface Chemistry and Surface Engineering, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_2074315              
8Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
9Sustainable Synthesis of Materials, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3289784              

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 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.

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Language(s): eng - English
 Dates: 2022-08-23
 Publication Status: Published in print
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41529-022-00281-x
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Title: npj Materials Degradation
  Abbreviation : npj Mater. Degrad.
Source Genre: Journal
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Pages: 71 Volume / Issue: 6 (1) Sequence Number: 71 Start / End Page: - Identifier: ISSN: 2397-2106
CoNE: https://pure.mpg.de/cone/journals/resource/2397-2106