Interstitial Segregation has the Potential to Mitigate Liquid Metal Embrittlement in Iron

Ahmadian, Ali; A,; Scheiber, Rolf; D, None; Zhou, Quan; X,; Gault,; B,; Romaner,; L,; Kamachali, Darvishi; R,; Ecker,; W,; Dehm,; G,; Liebscher, Christian; H, C

doi:10.1002/adma.202211796

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…This occurs due to cracking at GBs caused by the GB segregation of certain alloying elements or impurity atoms [7][8][9][10]. Therefore, the suppression of hydrogen embrittlement [5,[11][12][13][14][15][16][17][18], liquid metal embrittlement (LME) [19][20][21][22][23][24], and red hot embrittlement [25][26][27][28][29], all of which entail cracking at GBs, is a signi cant issue in the development of high-strength steels. Consequently, there is a critical need to design materials capable of suppressing the degradation of material properties and manufacturability associated with GB cracking.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the addition of trace amounts of B suppresses Zn penetration into GBs due to the segregation of B at these boundaries. This process strengthens the GBs and effectively suppresses LME [21]. Therefore, obtaining material design guidelines to suppress GB cracking necessitates a quantitative understanding of the extent of GB segregation of diverse solute elements.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals

Ito,

Yokoi,

Hyodo

et al. 2024

Preprint

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To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic-level control of general grain boundaries (GGBs), which govern the material properties, is achieved. However, owing to the complex and diverse structures of GGBs, there have been no reports on interatomic potentials capable of reproducing them. This accuracy is essential for conducting molecular dynamics analyses to derive material design guidelines. In this study, we constructed a machine learning interatomic potential (MLIP) with density functional theory (DFT) accuracy to model the energy, atomic structure, and dynamics of arbitrary grain boundaries (GBs), including GGBs, in α-Fe. Specifically, we employed a training dataset comprising diverse atomic structures generated based on crystal space groups. The GGB accuracy was evaluated by directly comparing with DFT calculations performed on cells cut near GBs from nano-polycrystals, and extrapolation grades of the local atomic environment based on active learning methods for the entire nano-polycrystal. Furthermore, we analyzed the GB energy and atomic structure in α-Fe polycrystals through large-scale molecular dynamics analysis using the constructed MLIP. Conventional interatomic potentials cannot accurately calculate the GB energy and atomic structure in α-Fe polycrystals. Conversely, the average GB energy of α-Fe polycrystals calculated by the constructed MLIP is 1.57 J/m2, exhibiting good agreement with experimental predictions. Our findings demonstrate the methodology for constructing an MLIP capable of representing GGBs with high accuracy, thereby paving the way for materials design based on computational materials science for polycrystalline materials.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals

Ito,

Yokoi,

Hyodo

et al. 2024

Preprint

View full text Add to dashboard Cite

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High‐Throughput First‐Principles Calculations and Machine Learning of Grain Boundary Segregation in Metals

Scheiber,

Razumovskiy,

Peil

et al. 2024

Adv Eng Mater

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The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the materials performance. However, the available computational data is scattered and inconsistent due to the use of different simulation parameters and methods. We present a high throughput study on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first‐principles methodology. The data is evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, W with the majority of the elements from the periodic table treated as segregating elements. Trends amongst the solute elements are analysed and explained in terms of phenomenological models and the computed data is compared to available literature data. The computed first‐principles data is used for a machine learning investigation showing the capabilities for extrapolation from first‐principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties.This article is protected by copyright. All rights reserved.

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Sandwich-structured In–Ga alloy phase change thermal pad with low thermal resistance and leakage prevention

Luo,

Huang,

Yang

et al. 2024

Rare Met.

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Interstitial Segregation has the Potential to Mitigate Liquid Metal Embrittlement in Iron

Cited by 8 publications

References 55 publications

Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals

Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals

High‐Throughput First‐Principles Calculations and Machine Learning of Grain Boundary Segregation in Metals

Sandwich-structured In–Ga alloy phase change thermal pad with low thermal resistance and leakage prevention

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