Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys
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Abstract
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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Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Corrosion Behavior and Inhibition · Nuclear Materials and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (56)
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