Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries

Tran, Richard; Xu, Zihan; Zhou, Naixie; Radhakrishnan, Balachandran; Luo, Jian; Ong, Shyue Ping

doi:10.1016/j.actamat.2016.07.005

Cited by 74 publications

(23 citation statements)

References 53 publications

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“…The near future DFT applications for investigations of material properties and for prediction of novel materials with tailored technological specifications may be foreseen, and has already started, in four basic directions (for each area we provide several references, which coin the path): 1) finite temperature effects, [37,38,125,127,129,[131][132][133][134]160] 2) extended defects (grain boundaries, stacking faults, dislocations, etc. ), [8,98,[193][194][195][196][197][198][199][200][201][202][203][204][205][206][207][208][209] 3) materials of relevance for real applications (complex compositions, realistic conditions, etc. ), [31,63,68,159,160,176,179,[210][211][212][213][214][215] and 4) high-throughput search for novel materials [13,[215]…”

Section: Discussionmentioning

confidence: 99%

Atomistic Modeling‐Based Design of Novel Materials

Holec¹,

Zhou

Riedl

et al. 2017

Adv Eng Mater

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Modern materials science increasingly advances via a knowledge-based development rather than a trialand-error procedure. Gathering large amounts of data and getting deep understanding of non-trivial relationships between synthesis of materials, their structure and properties is experimentally a tedious work. Here, theoretical modeling plays a vital role. In this review paper we briefly introduce modeling approaches employed in materials science, their principles and fields of application. We then focus on atomistic modeling methods, mostly quantum mechanical ones but also Monte Carlo and classical molecular dynamics, to demonstrate their practical use on selected examples. of the Deutsche Forschungsgemeinschaft (DFG). D.M. acknowledges the Collaborative Research Center SFB-TR 87/2 "Pulsed high power plasmas for the synthesis of nanostructured functional layers" of the DFG. The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).

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Section: Discussionmentioning

confidence: 99%

Atomistic Modeling‐Based Design of Novel Materials

Holec¹,

Zhou

Riedl

et al. 2017

Adv Eng Mater

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“…Surface slab structures from the Crystalium database, [33,37] which contains the precomputed surface energies and Wulff shapes of most elements in the Periodic Table. For 3. GB structures from our previous study of the effect of dopants on Mo GBs [32]. Specifically, DFT data from the relaxation of the (100) Σ5 twist and (310) Σ5 tilt, and static calculations of (110) Σ3 twist, (111) Σ3 tilt and (110) Σ11 twist boundaries were included.…”

Section: A Training Data Generationmentioning

confidence: 99%

Accurate force field for molybdenum by machine learning large materials data

Chen

Deng

Tran

et al. 2017

Phys. Rev. Materials

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109

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In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.

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“…△C and △R are the two most important factors to influence the strengthening energies. This may explain why the two-factor model is able to account for most of the variation in the strengthening energies in Mo GBs [28]. As shown in Figure 5, the features △H, △C and △R show a positive correlation with grain boundary embrittlement, while RS appears a negative correlation.…”

Section: Methodsmentioning

confidence: 91%

“…Except the similar common feature of ∆C or related quantities with previous modes, the ratio of bonding energies between the solute and solvent, captured by the ratio of their surface energies (RS), was emphasized in their model [27]. Instead of the traditional one-factor bond-breaking model that relates ∆E SE with relative cohesive energy, Tran et al used a simple two-factor linear model described by the relative metallic radii and the relative difference in cohesive energy, and found it is able to account for most of the variations in the ∆E SE with a value of r 2 > 0.79 [28].The above semi-empirical models and accurate first-principles calculations significantly advance the understanding of solute-induce changes of GB cohesion undoubtedly. However, these methods are limited either in terms of their accuracy or high computational cost.…”

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confidence: 99%

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Wang

et al. 2020

Materials

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The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.

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Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries

Cited by 74 publications

References 53 publications

Atomistic Modeling‐Based Design of Novel Materials

Atomistic Modeling‐Based Design of Novel Materials

Accurate force field for molybdenum by machine learning large materials data

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

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