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Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys
Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate... -
Machine learning for metallurgy: 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... -
Machine learning for metallurgy: a neural network potential for Al-Cu-Mg
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Pure Magnesium DFT calculations for interatomic potential fitting
This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun... -
Vanadium is an optimal element for strengthening in both fcc and bcc high-ent...
The element Vanadium (V) appears unique among alloying elements for providing high strengthening in both the fcc Co-Cr-Fe-Mn-Ni-V and bcc Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr high-entropy... -
Yield strength and misfit volumes of NiCoCr and implications for short-range-...
The face-centered cubic medium-entropy alloy NiCoCr has received considerable attention for its good mechanical properties, uncertain stacking fault energy, etc, some of which... -
Stress-dependence of generalized stacking fault energies: a DFT study
Generalized stacking fault energy (GSFE) is a crucial material property for describing nanoscale plasticity in crystalline materials, such as dislocation dissociation,... -
Machine learning for metallurgy: neural network potentials for Al-Cu-Mg and A...
Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest... -
Modeling of precipitate strengthening with near-chemical accuracy: case study...
Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condition where the strength-limiting processes of precipitate shearing and... -
Unified theory of atom-centered representations and message-passing machine-l...
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of... -
Finite-temperature materials modeling from the quantum nuclei to the hot elec...
Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be... -
Machine learning for metallurgy: 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... -
Pure Magnesium DFT calculations for interatomic potential fitting
This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun... -
DFT data for giant hardening response in AlMgZn(Cu) alloys
AiiDA calculations for the publication Giant hardening response in AlMgZn(Cu) alloys. This study presents a thermomechanical processing concept which is capable of exploiting... -
Machine learning for metallurgy: 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... -
Machine learning for metallurgy: 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... -
Origin of high strength in the CoCrFeNiPd high-entropy alloy
Recent experiments show that the CoCrFeNiPd high-entropy alloy (HEA) is significantly stronger than CoCrFeNi and with nanoscale composition fluctuations beyond those expected...
