Effect of alloying elements on the stacking fault energies of dilute al-based alloys

Gao, Qiuzhi; Zhang, H.; Rong, Yang; Fan, Z.; Liu, Y.; Wang, J.; Geng, Xinyu; Gao, Yuan; Shang, Shun‐Li; Du, Yong; Liu, Z.

doi:10.2298/jmmb180107007g

Cited by 10 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 2(b) shows that the γ SFE values of Pt 23 X increase and then decrease with increasing atomic number of X along different rows, where the maximum γ SFE values are observed in the alloys of Pt 23 Ni, Pt 23 Rh, and Pt 23 Ir for the 3d, 4d, and 5d alloying elements, respectively. The similar trends were also observed in our previous calculations for Al 23 X [34] and Ni 23 X [26]; indicating the critical role of atomic volume and atomic number of alloying elements to regulate γ SFE . For the facility of the ML study, stacking fault energy, γ SFE , is expressed as follows,…”

Section: Dft-based Results Of Stacking Fault Energy (γ Sfe )supporting

confidence: 89%

“…All DFT-based first-principles calculations in the present work were performed using the Vienna Ab initio Simulation Package (VASP) [54] for pure fcc elements and the Pt 23 X alloys. The γ SFE values of Al 23 X and Ni 23 X were predicted previously [26,34] using the same approach adopted in the present work. The ion-electron interaction in the present work was described by the projector augmented wave method [55] and the exchange-correlation functional was depicted by the generalized gradient approximation [56].…”

Section: Dft-based First-principles Calculationsmentioning

confidence: 88%

“…For instance, the γ SFE values affected by dilute alloying elements were predicted in Al alloys [34], Mg alloys [35], and Ni alloys [25,26] in terms of the alias shear deformations [25,26,34,35]. On the basis of empirical knowledge (instead of ML analysis), these works indicated that (i) the γ SFE values are closely related to atomic volumes of Al 23 X, Ni 23 X, and Mg 95 X as well as the position of alloying element X in the periodic table [26,34,35] and (ii) stacking fault energy can be understood qualitatively in terms of the (re)distribution and the shape of charge density [26,35]. These results suggest the elemental attributes of atomic number, volume, and valence electrons of alloying element X to correlate with stacking fault energy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Chong

Shang

Krajewski

et al. 2021

J. Phys.: Condens. Matter

View full text Add to dashboard Cite

Advances in machine learning (ML), especially in the cooperation between ML predictions, density functional theory (DFT) based first-principles calculations, and experimental verification are emerging as a key part of a new paradigm to understand fundamentals, verify, analyze, and predict data, and design and discover materials. Taking stacking fault energy (γ SFE) as an example, we perform a correlation analysis of γ SFE in dilute Al-, Ni-, and Pt-based alloys by descriptors and ML algorithms. These γ SFE values were predicted by DFT-based alias shear deformation approach, and up to 49 elemental descriptors and 21 regression algorithms were examined. The present work indicates that (i) the variation of γ SFE affected by alloying elements can be quantified through 14 elemental attributes based on their statistical significances to decrease the mean absolute error (MAE) in ML predictions, and in particular, the number of p valence electrons, a descriptor second only to the covalent radius in importance to model performance, is unexpected; (ii) the alloys with elements close to Ni and Co in the periodic table possess higher γ SFE values; (iii) the top four outliers of DFT predictions of γ SFE are for the alloys of Al23La, Pt23Au, Ni23Co, and Al23Be based on the analyses of statistical differences between DFT and ML predictions; and (iv) the best ML model to predict γ SFE is produced by Gaussian process regression with an average MAE < 8 mJ m−2. Beyond detailed analysis of the Al-, Ni-, and Pt-based alloys, we also predict the γ SFE values using the present ML models in other fcc-based dilute alloys (i.e., Cu, Ag, Au, Rh, Pd, and Ir) with the expected MAE < 17 mJ m−2 and observe similar effects of alloying elements on γ SFE as those in Pt23X or Ni23X.

show abstract

Section: Dft-based Results Of Stacking Fault Energy (γ Sfe )supporting

confidence: 89%

Section: Dft-based First-principles Calculationsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Chong

Shang

Krajewski

et al. 2021

J. Phys.: Condens. Matter

View full text Add to dashboard Cite

show abstract

“…Moreover, it has been proposed that decreasing stacking fault energy can promote the transformation of deformationinduced microstructures. Q. Gao et al 99) calculated that adding Si, Sc, Ga, Ge, Sr, Zr, In, Sn, La, Hf, Pb, and other alloying elements would reduce the stacking fault energy of aluminum. These findings lay the groundwork for future efforts to manipulate deformation-induced microstructure through elemental doping.…”

Section: Regulation By Alloyingmentioning

confidence: 99%

Hydrolytic Hydrogen Production from Severely Plastic Deformed Aluminum-Based Materials: An Overview

et al. 2023

View full text Add to dashboard Cite

The development of hydrogen energy will help to reduce the use of nonrenewable energy sources and achieve global carbon neutrality. The aluminum-water reaction is an important method of producing hydrogen because aluminum has abundant reserves, a high yield, and no pollution. However, the dense passive oxide film on the surface of aluminum, on the other hand, often obstructs this reaction, which is the primary issue limiting the development of aluminum-based hydrolytic materials. Mechanochemical activation by processing severely plastic deformed aluminum-based materials is one effective approach and has been developed in recent years. This article reviews recent progress of hydrogen production from hydrolysis of severely plastic deformed aluminum-based materials. The kinetic model of aluminum-water reaction, aging protection of the materials, catalytic mechanism and stable rate control for the hydrolysis of aluminum-based materials are reviewed. Furthermore, some existing problems as well as some suggestions for future research on hydrogen production from aluminum-based materials are also discussed.

show abstract

“…Stacking fault energy of pure aluminium is normally high and relatively lower for its alloys, thus addition of alloying elements has shown a reduction in this energy resulting in easier formation of partial dislocations and deformation-induced twinning [85,86]. The current alloy has silicon, copper and magnesium as alloying elements which not only reduce the stacking fault energy of aluminum, but also provide additional sources for dislocations and thus strengthening [87,88].…”

Section: Strength Evolutionmentioning

confidence: 99%

Microstructural and hardness evolution of additively manufactured Al–Si–Cu alloy processed by high-pressure torsion

et al. 2022

View full text Add to dashboard Cite

Nanostructured Al-9%Si-3%Cu alloy was achieved by direct metal laser sintering (DMLS) and then processed using high-pressure torsion (HPT) processing, which resulted in considerable grain refinement down to 60 nm associated with a substantial dislocation density up 6.2 × 1014 m−2 and a significant reduction in the porosity. Hardness measurements across the horizontal and vertical cross sections showed an improvement in the strength homogeneity for processed samples after 10 turns of HPT processing. These results indicate that a controllable ultrafine-grained microstructure can be achieved by employing additive manufacturing, followed by effective severe plastic deformation processing.

show abstract

Effect of alloying elements on the stacking fault energies of dilute al-based alloys

Abstract: A systematic study of the stacking fault energy (γ SF

Cited by 10 publications

References 48 publications

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Hydrolytic Hydrogen Production from Severely Plastic Deformed Aluminum-Based Materials: An Overview

Microstructural and hardness evolution of additively manufactured Al–Si–Cu alloy processed by high-pressure torsion

Contact Info

Product

Resources

About