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Albert Johann Glensk

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Related publications (3)

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SDSC - Administration

EPFL
VPA-AVP-CP
SDSC
SDSC-GE

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

William Curtin, Michele Ceriotti, Albert Johann Glensk, Daniel John Gilles Marchand, Abhinav Jain

High-strengthmetal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations ...

2021

Machine learning for metallurgy I. A neural-network potential for Al-Cu

William Curtin, Albert Johann Glensk, Daniel John Gilles Marchand, Abhinav Jain

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 properties, are inherently atomic scale phenomena, particularly during ...

2020

Phonon Lifetimes Throughout the Brillouin Zone at Elevated Temperatures from Experiment and ab initio

Albert Johann Glensk

We obtain phonon lifetimes in aluminium by inelastic neutron scattering experiments, by ab initio molecular dynamics, and by perturbation theory. At elevated temperatures significant discrepancies are found between experiment and perturbation theory, which ...

2019