Publications de Daniel John Gilles Marchand

Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn

William Curtin, Daniel John Gilles Marchand

Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest cannot be measured experimentally. Accurate simulation methods are essential but f ...

2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

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 ...

EPFL2022

Giant hardening response in AlMgZn(Cu) alloys

William Curtin, Daniel John Gilles Marchand

This study presents a thermomechanical processing concept which is capable of exploiting the full industrial application potential of recently introduced AlMgZn(Cu) alloys. The beneficial linkage of alloy design and processing allows not only to satisfy th ...

PERGAMON-ELSEVIER SCIENCE LTD2021

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

Albert Johann Glensk, William Curtin, Michele Ceriotti, 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

Wannier90 as a community code: new features and applications

Nicola Marzari, Giovanni Pizzi, Marco Gibertini, Lorenzo Paulatto, Hyungjun Lee, Daniel John Gilles Marchand, Antimo Marrazzo, Samuel Poncé, Junfeng Qiao

Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representi ...

2020

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

Albert Johann Glensk, William Curtin, 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