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Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships
Linear and non-linear quantitative structure?property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods. The consensus GA-PLS model comprised of eight variables (exponential of the calculated adsorption energy, LUMO, van der Waals surface area and volume, polarizability, electron affinity, electrophilicity, electron donor capacity) exhibited an %RMSECV of 16.5% and mean %RMSE of 14.9%. Such a model moderately captured the complex relationships between inhibition efficiency and the quantum chemical variables. The consensus GA-ANN model comprised of nine input variables (exponential of the calculated adsorption energy, HOMO, LUMO, HOMO-LUMO Gap, electronegativity, softness, electrophilicity, electron donor capacity and N atomic charge) exhibited an %RMSECV of 16.7 ¡À 2.3% and mean %RMSE (training/testing/validation) of 8.8%, performing better than its linear counterpart in terms of predictive ability. Both models revealed the importance of adsorption to the metal surface, and electronic parameters quantifying electron acceptance/donation to/from the iron surface, suggesting key corrosion inhibition design principles.
Balanced chemical reaction does not necessarily reveal either the individual elementary reactions by which a reaction occurs or its rate law.Electric Literature of 5096-73-1. In my other articles, you can also check out more blogs about 5096-73-1
Reference£º
Pyridazine – Wikipedia,
Pyridazine | C4H4N2066 – PubChem