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 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 …
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 …
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 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 …
Machine learning for metallurgy V: A neural-network potential for zirconium
The mechanical performance—including deformation, fracture and radiation damage—of
zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the …
zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the …
Interatomic potential for the Al-Cu system
F Apostol, Y Mishin - Physical Review B, 2011 - APS
An angular-dependent interatomic potential has been developed for the Al-Cu system based
on existing embedded-atom method potentials for Al and Cu and fitting of the cross …
on existing embedded-atom method potentials for Al and Cu and fitting of the cross …
Stratified construction of neural network based interatomic models for multicomponent materials
Recent application of neural networks (NNs) to modeling interatomic interactions has shown
the learning machines' encouragingly accurate performance for select elemental and …
the learning machines' encouragingly accurate performance for select elemental and …
Incorporating first-principles energetics in computational thermodynamics approaches
Computational thermodynamic approaches have become a valuable tool in the calculation
of complex, multicomponent phase equilibria often found in industrial alloys. These methods …
of complex, multicomponent phase equilibria often found in industrial alloys. These methods …
Atomic-scale simulations in multi-component alloys and compounds: A review on advances in interatomic potential
F Wang, HH Wu, L Dong, G Pan, X Zhou… - Journal of Materials …, 2023 - Elsevier
Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …
but the vast range of possible compositions and microstructures makes it challenging to …