Atomistic simulation of initial stages of iron corrosion in pure water using reactive molecular dynamics

DorMohammadi, H.; Pang, Qin; Árnadóttir, Líney; Isgor, O. Burkan

doi:10.1016/j.commatsci.2017.12.044

Cited by 51 publications

(24 citation statements)

References 52 publications

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“…The initial passive film is created using ReaxFF-MD simulations starting with a Fe(110) structure (24.61 Å × 20.40 Å × 22.87 Å) containing 1080 Fe atoms, in 0.316 M NaOH solution (pH~13.5), containing 21 Na and OH ions distributed evenly in equally spaced seven layers of 348 water molecules. Previous studies have shown that different surface orientations do not affect the oxidation behavior of iron significantly [50][51][52][53][54][55] ; therefore, the Fe(110) is used here to represent a closely packed iron surface. Periodic boundary conditions were applied along the cross-sectional plane of the iron domain, while a fixed boundary condition was imposed along the longitudinal direction.…”

Section: Passive Filmmentioning

confidence: 99%

“…We validated these parameters (that are required for determining the bond order, bond energy, valence angle energy, torsional angle energy, and van der Waals energy) by comparing ReaxFF-MD simulations of the surface formation energy and water adsorption energy on the Fe(110) surface with DFT calculations. 50 All ReaxFF-MD simulations were performed at room temperature (300 K) under standard atmospheric pressure (1 atm). The initial distance between molecules and ions in the electrolyte and the dimensions of the vacuum slab were determined based on the density of the solution at 300 K and Fig.…”

Section: Reaxff-md Simulationsmentioning

confidence: 99%

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Investigation of chloride-induced depassivation of iron in alkaline media by reactive force field molecular dynamics

et al. 2019

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The passivity of iron in alkaline media enables the use of carbon steel as reinforcement in concrete, which makes up the majority of modern infrastructure. However, chlorides, mainly from deicing chemicals or marine salts, can break down the iron passive film and cause active corrosion. Despite recent advances in nanoscale characterization of iron passivity, significant gaps exist in our understanding of the dynamic processes that lead to the chloride-induced breakdown of passive films. In this study, chlorideinduced depassivation of iron in pH 13.5 NaOH solution is studied using reactive force field molecular dynamics. The depassivation process initiates by local acidification of the electrolyte near the film surface, followed by iron dissolution into the electrolyte, and iron vacancy formation in the passive film. Chlorides do not penetrate the passive film, but mainly act as a catalyst for the formation of iron vacancies, which diffuse toward the metal/oxide interface, suggesting a depassivation mechanism consistent with the pointdefect model.

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Section: Passive Filmmentioning

confidence: 99%

Section: Reaxff-md Simulationsmentioning

confidence: 99%

Investigation of chloride-induced depassivation of iron in alkaline media by reactive force field molecular dynamics

et al. 2019

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“…Global costs for corrosion treatment reach 3.4% of global GDP or about US$ 2.5 trillion per year [8] [9]. The increase in these costs over the last decade has continued to increase [10]. Therefore, corrosion needs to be controlled.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Natural Extracts as Green Corrosion Inhibitors in Steel Using Density Functional Theory

Akrom¹

2022

J. Teori Apl. Fis.

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Steel is a material that has low resistance to corrosion when interacting with a corrosive environment. The application of natural extracts as green inhibitors is able to provide good performance in inhibiting corrosion of steel with high inhibition efficiency. Natural extracts that are effective and efficient as corrosion inhibitors on steel are those which in their compound structure contain heteroatom groups (such as O, N, S, P) and aromatic rings. This work provides an important comparative overview for the development of green inhibitor natural extracts in steel. The results of theoretical studies based on quantum mechanics with the DFT method at the atomic level based on molecular orbitals, chemical quantum parameters, and adsorption characteristics show results that are in accordance with experimental studies. The frontier molecular orbital (FMO) plot shows the distribution of electron density in the HOMO-LUMO region as a predictor of the active site of the inhibitor molecule interacting with the steel surface. Quantum chemical parameters such as ionization potential (I), electron affinity (A), absolute electronegativity (χ), hardness (η), softness (σ), fraction of electrons transferred (ΔN), electrophilicity (ɷ), and electron backdonation (ΔEback-donation) was calculated to obtain a correlation between the electronic properties of the inhibitor molecule and the corrosion inhibition potential. The results of the calculation of the quantum chemical parameters show the reactivity of the inhibitor molecule which has a very good potential to interactand bind strongly to the steel surface. This has the potential to make the inhibitor molecule have a high inhibition efficiency. Chemical adsorption and/or physical adsorption by forming complex compounds between inhibitor molecules and the steel surface are corrosion inhibition mechanisms to protect steel from a corrosive environment.The development of future studies should be able to display the mechanism of interaction and inhibition of inhibitor molecules in more detail and systematically at the atomic level on several metal surfaces such as Fe, Al, Cu, and others.

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“…With the enormous rise in computational power and the number of molecular simulation methods in the past decades, atomistic modeling is increasingly becoming the method of choice. [1][2][3][4][5][6][7][8][9] Applications range from the study and prevention of corrosion [10][11][12] to protein folding, 13 unfolding, 14 and self-assembly. 15 For many applications, machine learning force fields (MLFFs) are becoming the method of choice, as they can potentially reproduce any functional form of interatomic and intermolecular interactions, leading to reliable descriptions of potential energy surfaces (PESs) of arbitrary complexity.…”

Section: Introductionmentioning

confidence: 99%

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

Fonseca

Poltavsky

Vassilev-Galindo

et al. 2021

The Journal of Chemical Physics

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The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to twofold decrease in the root mean squared errors for force predictions on non-equilibrium geometries of these molecules. Furthermore, our ML models demonstrate superior stability over the default training approaches, allowing reliable study of processes involving highly out-of-equilibrium molecular configurations. These results hold for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model).

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Atomistic simulation of initial stages of iron corrosion in pure water using reactive molecular dynamics

Cited by 51 publications

References 52 publications

Investigation of chloride-induced depassivation of iron in alkaline media by reactive force field molecular dynamics

Investigation of chloride-induced depassivation of iron in alkaline media by reactive force field molecular dynamics

Investigation of Natural Extracts as Green Corrosion Inhibitors in Steel Using Density Functional Theory

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

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