Angular-dependent interatomic potential for the binary Ni–Cr system

Howells, Chris

doi:10.1088/1361-651x/aae400

Cited by 34 publications

(10 citation statements)

References 51 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The amended Finnis Sinclair potential reported that this potential gave a good agreement of the predicted formation energies of mono-vacancy and self-interstitial of octahedral interstitial site with experimental and theoretical data confirms the validity of the amended Finnis Sinclair potential in pure V (Finnis and Sinclair, 1984). Furthermore, the agreement of defect properties and mechanical properties of this alloy (V-4Ti-4Cr) with experimental or other theoretical data also support the applicability of the amended Finnis Sinclair potential in Vrich ternary V-Ti-Cr alloys (Howells and Mishin, 2018;Daw and Baskes, 1984). In this work, the amended Finnis Sinclair potential was adopted as a reference potential to investigate thermo-mechanical properties of the V-5Cr-5Ti alloy.…”

Section: The Interatomic Potential For V-5cr-5ti Ternary Alloysupporting

confidence: 78%

Thermo-mechanical properties of V-5Cr-5Ti alloy: 3D molecular dynamics simulation

Aish¹,

Aljasar²

2020

Int. J. Phys. Sci.

View full text Add to dashboard Cite

This paper presents the results of the study of 3D molecular dynamics simulation to investigate the thermomechanical properties of the V-5Cr-5Ti ternary alloy. The main purpose of this work is to evaluate the quality of the Finnis-Sinclair potential for V-rich VCr -Ti random alloys with a bodycentered-cubic (bcc) structure. The results demonstrated that the elastic constants of the V-5Cr-5Ti alloy are close to the corresponding value of pure metal vanadium. The two-phase method coexistence solid-liquid was applied to determine the melting point of this alloy at standard pressure. The equilibrium melting point was found at 2237 K, slightly higher than the melting point of Vanadium at the same pressure.

show abstract

Section: The Interatomic Potential For V-5cr-5ti Ternary Alloysupporting

confidence: 78%

Thermo-mechanical properties of V-5Cr-5Ti alloy: 3D molecular dynamics simulation

Aish¹,

Aljasar²

2020

Int. J. Phys. Sci.

View full text Add to dashboard Cite

show abstract

“…Using the accelerated ML model outlined in Fig. 3, we compute GB segregation energy spectra for 18 solutes 37,40,42,53,58,[75][76][77][78][79][80][81] in a 20x20x20 nm 3 Ni polycrystal of grain size 10 nm; the anti-segregation region (ΔE seg i >0) is shaded. The spectra are fitted to the skew-normal function Eq.…”

Section: Discussionmentioning

confidence: 99%

Learning grain boundary segregation energy spectra in polycrystals

2020

View full text Add to dashboard Cite

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

show abstract

“…The melting point calculation used the solid-liquid phase coexistence method [52,[62][63][64]. A simulation block containing the solid and liquid phases separated by a planar interface was coupled to a thermostat.…”

Section: Resultsmentioning

confidence: 99%

Development of a physically-informed neural network interatomic potential for tantalum

Lin

Pun

2021

Preprint

View full text Add to dashboard Cite

Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning (ML) methods. ML potentials predict the energy and forces by numerical interpolation using a large reference database generated by quantum-mechanical calculations. While high accuracy of interpolation can be achieved, extrapolation to unknown atomic environments is unpredictable. The recently proposed physically-informed neural network (PINN) model significantly improves the transferability by combining a neural network regression with a physics-based bond-order interatomic potential. Here, we demonstrate that general-purpose PINN potentials can be developed for body-centered cubic (BCC) metals. The proposed PINN potential for tantalum reproduces the reference energies within 2.8 meV/atom. It accurately predicts a broad spectrum of physical properties of Ta, including (but not limited to) lattice dynamics, thermal expansion, energies of point and extended defects, the dislocation core structure and the Peierls barrier, the melting temperature, the structure of liquid Ta, and the liquid surface tension. The potential enables large-scale simulations of physical and mechanical behavior of Ta with nearly first-principles accuracy while being orders of magnitude faster. This approach can be readily extended to other BCC metals.

show abstract

Angular-dependent interatomic potential for the binary Ni–Cr system

Cited by 34 publications

References 51 publications

Thermo-mechanical properties of V-5Cr-5Ti alloy: 3D molecular dynamics simulation

Thermo-mechanical properties of V-5Cr-5Ti alloy: 3D molecular dynamics simulation

Learning grain boundary segregation energy spectra in polycrystals

Development of a physically-informed neural network interatomic potential for tantalum

Contact Info

Product

Resources

About