Reviewing machine learning of corrosion prediction in a data-oriented perspective

Coelho, Leonardo Bertolucci; Zhang, Dawei; Ingelgem, Yves Van; Steckelmacher, Denis; Nowé, Ann; Terryn, Herman

doi:10.1038/s41529-022-00218-4

Cited by 61 publications

(39 citation statements)

References 59 publications

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“…highlighted the power of unsupervised algorithms on the examples clustering of particle distributions, biological tissues, and atomic structures, that is still, however, poorly exploited in electrochemistry. [39,42,[46][47][48] Indeed, such automatized data treatment will provide various local descriptors for each (and all) nanoobjects imaged which can be used to reconstruct the global electrochemical response and in turns unveil the nanoscale contribution contained in an electrode response.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

et al. 2022

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The nanostructuration of an electrochemical interface dictates its micro‐ and macroscopic behavior. It is generally highly complex and often evolves under operating conditions. Electrochemistry at these nanostructurations can be imaged both operando and/or ex situ at the single nanoobject or nanoparticle (NP) level by diverse optical, electron, and local probe microscopy techniques. However, they only probe a tiny random fraction of interfaces that are by essence highly heterogeneous. Given the above background, correlative multimicroscopy strategy coupled to electrochemistry in a droplet cell provides a unique solution to gain mechanistic insights in electrocatalysis. To do so, a general machine‐vision methodology is depicted enabling the automated local identification of various physical and chemical descriptors of NPs (size, composition, activity) obtained from multiple complementary operando and ex situ microscopy imaging of the electrode. These multifarious microscopically probed descriptors for each and all individual NPs are used to reconstruct the global electrochemical response. Herein the methodology unveils the competing processes involved in the electrocatalysis of hydrogen evolution reaction at nickel based NPs, showing that Ni metal activity is comparable to that of platinum.

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Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

et al. 2022

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“…Data driven technologies and machine leaning are among the latest developments and most promising approaches in corrosion science to guide the discovery and design of more effective and environmentally benign corrosion inhibitors and protective coating systems 3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . However, one of the main challenges dealing with the application of machine learning to understand and design protective systems is building the datasets required for training the predictive models 26,27 . The collection of experimental data, as well as data management and curation, are among the most time-consuming tasks in the machine learning workflow.…”

Section: Resultsmentioning

confidence: 99%

CORDATA: an open data management web application to select corrosion inhibitors

et al. 2022

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“…Recently, many scientific fields applied machine learning to multidisciplinary prediction 17 – 19 . Even in the field of corrosion, many scientists have applied machine learning to predict the corrosion rate in the atmosphere, the performance of corrosion inhibitors, and corrosion behavior 20 – 24 . However, there are not many studies that focus on predicting the corrosion rate of carbon steel canisters in the soil environment.…”

Section: Introductionmentioning

confidence: 99%

Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil

Chung

Yoo

et al. 2022

Sci Rep

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Soil corrosion is always a critical concern to corrosion engineering because of the economic influence of soil infrastructures as has been and has recently been the focus of spent nuclear fuel canisters. Besides corrosion protection, the corrosion prediction of the canister is also important. Advanced knowledge of the corrosion rate of spent nuclear fuel canister material in a particular environment can be extremely helpful in choosing the best protection method. Applying machine learning (ML) to corrosion rate prediction solves all the challenges because of the number of variables affecting soil corrosion. In this study, several algorithms of ML, including series individual, boosting, bagging artificial neural network (ANN), series individual, boosting, bagging Chi-squared automatic interaction detection (CHAID) tree decision, linear regression (LR) and an ensemble learning (EL) merge the best option that collects from 3 algorithm methods above. From the performance of each model to find the model with the highest accuracy is the ensemble stacking method. Mean absolute error performance matrices are shown in Fig. 15. Besides applying ML, the significance of the input variables was also determined through sensitivity analysis using the feature importance criterion, and the carbon steel corrosion rate is the most sensitive to temperature and chloride.

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Reviewing machine learning of corrosion prediction in a data-oriented perspective

Cited by 61 publications

References 59 publications

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

CORDATA: an open data management web application to select corrosion inhibitors

Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil

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