Correlation between molecular features and electrochemical properties using an artificial neural network

Chen, Fiona Fang; Breedon, Michael; White, P. A.; Chu, Clement; Mallick, Dwaipayan; Thomas, Sebastian; Sapper, Erik D.; Cole, Ivan

doi:10.1016/j.matdes.2016.09.084

Cited by 34 publications

(13 citation statements)

References 27 publications

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“…With the development of high-throughput synthesis and screening coupled with machine learning and other computational techniques, the structure− function relationships for corrosion inhibitors will be better understood, hopefully leading to the ability to predict the next generation of corrosion inhibitors. 45,47,48 ■ CONCLUSIONS Two libraries of organic corrosion inhibitors based on biologically derived TAL and 4HC starting molecules were successfully synthesized. Relative to the HMTA reference inhibitor molecule, 12 of the synthesized molecules showed high corrosion inhibition performance for mild steel in a sulfuric acid solution as characterized by EIS (IE values of 78% or greater) and polarization testing.…”

Section: ■ Experimental Methodsmentioning

confidence: 99%

Triacetic Acid Lactone and 4-Hydroxycoumarin as Bioprivileged Molecules for the Development of Performance-Advantaged Organic Corrosion Inhibitors

Huo

Bradley

Podolak

et al. 2022

ACS Sustainable Chem. Eng.

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Biomass conversion, especially the development of bioprivileged molecules utilizing integration of biological and chemical processes, has shown potential to produce novel chemicals with enhanced properties. Here, organic corrosion inhibitors based on triacetic acid lactone (TAL) and 4-hydroxycoumarin (4HC) have been synthesized in good yield and tested for corrosion inhibition on mild steel in both sulfuric acid and hydrochloric acid. Sixteen novel corrosion inhibitors derived from TAL and 4HC were efficiently synthesized, and 12 of them showed high corrosion inhibition efficiency as confirmed by electrochemical impedance spectroscopy (giving values greater than 78%) and polarization analysis. While TAL-based compounds showed good corrosion inhibition performance, the 4HC-based compounds showed further improvement in corrosion inhibition performance. Scanning electron microscopy analysis of the mild steel coupon in the presence of selected inhibitors showed that corrosion was significantly diminished, with complementary X-ray photoelectron spectroscopy analysis suggesting that the inhibitor molecules strongly adsorbed on the steel surface. Quantum chemical calculations showed poor correlation between calculated parameters and the performance of these molecules. This large set of corrosion inhibitors containing a dozen promising corrosion inhibitors is useful for future rational design of corrosion inhibitors. The results demonstrate a new direction for the development of bioprivileged molecules.

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Section: ■ Experimental Methodsmentioning

confidence: 99%

Triacetic Acid Lactone and 4-Hydroxycoumarin as Bioprivileged Molecules for the Development of Performance-Advantaged Organic Corrosion Inhibitors

Huo

Bradley

Podolak

et al. 2022

ACS Sustainable Chem. Eng.

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“…Experimental approaches alone cannot possibly explore more than a tiny fraction of the vast space of compounds with potentially useful dissolution modulating properties, despite impressive developments in high throughput techniques [29][30][31][32] . Fortunately, datadriven computational methods [33][34][35][36][37][38][39][40] can efficiently explore larger areas of chemical space with orders of magnitude less time and effort. Hence, they offer a very efficient way to preselect a short list of promising candidates prior to experimental investigation.…”

Section: Introductionmentioning

confidence: 99%

Exploring structure-property relationships in magnesium dissolution modulators

et al. 2021

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Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.

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“…According to Antonijevic and Petrovic (2015) [ 9 ], the copper atom presents vacant d orbitals that form bonds with heteroatoms that donate electrons or generate an interaction with rings containing conjugated bonds, π electrons. The complex forms a protective film on the metallic surface that blocks aggressive ions [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, it is necessary to develop other alternatives to better understand corrosion phenomena, reduce time, the number of experiments, as well as control the process. ANN models represent a good option to describe corrosion behavior [ 12 , 13 ]. This kind of a model is based on the biological functions of the brain where connections of neurons form a network.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN)

Millán-Ocampo

Parrales

Martı́nez

et al. 2018

Entropy

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Abstract:In this work, three models based on Artificial Neural Network (ANN) were developed to describe the behavior for the inhibition corrosion of bronze in 3.5% NaCl + 0.1 M Na 2 SO 4 , using the experimental data of Electrochemical Impedance Spectroscopy (EIS). The database was divided into training, validation, and test sets randomly. The parameters process used as the inputs of the ANN models were frequency, temperature, and inhibitor concentration. The outputs for each ANN model and the components in the EIS spectrum (Z re , Z im , and Z mod ) were predicted. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and linear in the output layer, while the Levenberg-Marquardt algorithm was applied to determine the optimum values of the weights and biases. The statistical analysis of the results revealed that ANN models for Z re , Z im , and Z mod can successfully predict the inhibition corrosion behavior of bronze in different conditions, where what was considered included variability in temperature, frequency, and inhibitor concentration. In addition, these three input parameters were keys to describe the behavior according to a sensitivity analysis.

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Correlation between molecular features and electrochemical properties using an artificial neural network

Cited by 34 publications

References 27 publications

Triacetic Acid Lactone and 4-Hydroxycoumarin as Bioprivileged Molecules for the Development of Performance-Advantaged Organic Corrosion Inhibitors

Triacetic Acid Lactone and 4-Hydroxycoumarin as Bioprivileged Molecules for the Development of Performance-Advantaged Organic Corrosion Inhibitors

Exploring structure-property relationships in magnesium dissolution modulators

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN)

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