From DFT to precipitation and strengthening in Aluminum Alloys

William Curtin

EPFL, Switzerland


Professor Curtin earned a 4 yr. ScB/ScM degree in Physics from Brown University in 1981 and a PhD in theoretical physics from Cornell University in 1986. He worked as staff researcher at British Petroleum until 1993 and then joined Virginia Tech as an Associate Professor in both Engineering Mechanics and Materials Science. In 1998 he returned to Brown as Full Professor of Engineering in the Solid Mechanics group, where he was appointed Elisha Benjamin Andrews Professor in 2006. He joined Ecole Polytechnique Federale de Lausanne as the Director of the Institute of Mechanical Engineering in 2011 and officially as Full Professor in 2012.

His research successes include predictive theories of optical properties of nanoparticles, statistical mechanics of freezing, hydrogen storage in amorphous metals, strength and toughness of fiber composites, dynamic strain aging and ductility in lightweight Al and Mg metal alloys, solute strengthening of metal alloys including high entropy alloys, and hydrogen embrittlement of metals, along with innovative multiscale modeling methods to tackle many of these problems.

Professor Curtin was a Guggenheim Fellow in 2005-06, was Editor-in-Chief of “Modeling and Simulation in Materials Science and Engineering” from 2006-2016, has published over 280 journal papers that have received over 18500 citations with an h-index of 74 (Google Scholar), and has been the Principal Investigator on over $37M of funded research projects.


From DFT to precipitation and strengthening in Aluminum Alloys

Industrial processing and application of advanced materials requires exquisite control of both composition and processing path to achieve optimal performance.  Bridging the chasm between chemical interactions and macroscopic material behavior can be aided by emerging integrated multiscale materials modelling approaches.  Here, we illustrate progress in the domain of metallurgy, presenting a multiscale pathway from first-principles modelling to alloy evolution during processing to alloy yield strength for Al-Mg-Si Al-6xxx alloy.

A first-principles database of many metallurgically-relevant structures is created and used to develop a Neural Network interatomic potential (NNP) for the Al-Mg-Si system [1].  The NNP is then used in a Kinetic Monte Carlo study of natural aging to demonstrates that early-stage clusters trap vacancies and delay further evolution at room temperature.  The NNP is then further used to compute the Generalized Stacking Fault Energy surfaces for the various β” precipitates formed at peak aging [1], and direct atomistic simulations at experimental scales show the shearing and Orowan looping that control alloy strength [2].

Finally, a mesoscale discrete dislocation dynamics method is calibrated to atomistic NNP quantities and used to simulate Orowan looping in realistic 3d precipitate microstructures [2].  While a seamless multiscale path is not yet complete, our progress to date shows how new machine learning potentials provide a crucial quantitative connection between quantum and meso scales.


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