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Synchrotron X-ray video of full-penetration laser welding of aluminum AA1050A
The video shows a synchrotron x-ray video of full-penetration laser welding of the aluminum alloy AA1050A (Al99.5). At the beginning of the video the transition from partial... -
ISS Rhodotorula mucilaginosa Genomes
Rhodotorula mucilaginosa genomes isolated from 8 locations in the ISS over 2 flights (MT-1) -
Polymer electrolyte morphology investigated by SANS
Polymer electrolytes constitute a promising class of materials for rechargeable Li-batteries. The ionic conductivity behavior is dependent on the local morphology of the... -
Aluminum alloy compositions and properties extracted from a corpus of scienti...
Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help... -
Aluminum alloy compositions and properties extracted from a corpus of scienti...
Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help... -
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... -
Aluminum alloy compositions and properties extracted from a corpus of scienti...
Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help... -
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... -
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... -
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... -
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... -
Adatom-Induced Local Melting
We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a...
