Machine learning for metallurgy III: A neural network potential for Al-Mg-Si
High-strength metal alloys achieve their performance via careful control of the nucleation,
growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by …
growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by …
Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn
D Marchand, WA Curtin - Physical Review Materials, 2022 - APS
Most metallurgical properties, eg, dislocation propagation, precipitate formation, can only be
fully understood atomistically but most phenomena and quantities of interest cannot be …
fully understood atomistically but most phenomena and quantities of interest cannot be …
Neural network potential for Al-Mg-Si alloys
The 6000 series Al alloys, which include a few percent of Mg and Si, are important in
automotive and aviation industries because of their low weight, as compared to steels, and …
automotive and aviation industries because of their low weight, as compared to steels, and …
Machine learning for metallurgy I. A neural-network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and
solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical …
solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical …
Machine learning for metallurgy II. A neural-network potential for magnesium
Interatomic potentials are essential for studying fundamental mechanisms of deformation
and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are …
and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are …
[HTML][HTML] Modeling of precipitate strengthening with near-chemical accuracy: Case study of Al-6xxx alloys
Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-
aged condition where the strength-limiting processes of precipitate shearing and Orowan …
aged condition where the strength-limiting processes of precipitate shearing and Orowan …
Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential
Various industrial/commercial applications use Al-Mg alloys, yet the Mg added to Al
materials, to improve strength, is susceptible to surface segregation and oxidation, leaving …
materials, to improve strength, is susceptible to surface segregation and oxidation, leaving …
Density Functional Study of the β″ Phase in Al-Mg-Si Alloys
PH Ninive, OM Løvvik, A Strandlie - Metallurgical and Materials …, 2014 - Springer
The β ″phase is the major hardening precipitate in Al-Mg-Si alloys. It was studied by
atomistic calculations based on density functional theory (DFT), using an atomistic model …
atomistic calculations based on density functional theory (DFT), using an atomistic model …
3D modelling of β′′ in Al–Mg–Si: Towards an atomistic level ab initio based examination of a full precipitate enclosed in a host lattice
FJH Ehlers, S Dumoulin, R Holmestad - Computational materials science, 2014 - Elsevier
We extend a first principles based hierarchical multi-scale model scheme for describing a
fully coherent precipitate in a host lattice to 3D simulations. As our test system, the needle …
fully coherent precipitate in a host lattice to 3D simulations. As our test system, the needle …
Ab initio simulations of clustering and precipitation in Al–Mg–Si alloys
N Sandberg, M Slabanja, R Holmestad - Computational materials science, 2007 - Elsevier
A class of proposed coherent precipitate structures (Guinier–Preston zones) in the Al–Mg–Si
alloy are investigated using first-principles density functional theory methods. The cluster …
alloy are investigated using first-principles density functional theory methods. The cluster …