D&D2 - Metal Alloys

The ultimate objective of the project D&D2 — From Atoms to Additive Manufacturing: Computational Design of Complex Metal Alloys — is to develop a computational framework for the creation and control of precipitate-strengthened metal alloys, with a major application goal of guiding emerging additive manufacturing methodologies. The technical objectives that underly this ultimate objective are to predict a priori, and validate experimentally, the structure and energetics of complex precipitate phases that form in advanced engineering metal alloys; the resulting mechanical strength (plastic flow stress) as a function of precipitate morphology and alloy chemistry; and the early-stage evolution of the alloy as a function of time-temperature history.

The project is led by William Curtin.

Group Leaders

William Curtin

William Curtin

Project leader

EPFL, Lausanne

Michele Ceriotti

Michele Ceriotti

Project leader

EPFL, Lausanne

Anatole von Lilienfeld

Anatole von Lilienfeld

Project leader

UniBas, Basel

View people involved

Related publications (until January 2020)

Y. Hu, B. Szajewski, D. Rodney, W. Curtin, Atomistic dislocation core energies and calibration of non-singular discrete dislocation dynamics, Modelling and Simulation in Materials Science and Engineering 28, 015005 (2020).
Group(s): Curtin / Project(s): DD2
P. Andric, B. Yin, W. A. Curtin, Stress-dependence of generalized stacking fault energies, Journal of the Mechanics and Physics of Solids 122, 262–279 (2019). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
F. Musil, M. J. Willatt, M. A. Langovoy, M. Ceriotti, Fast and Accurate Uncertainty Estimation in Chemical Machine Learning, Journal of Chemical Theory and Computation 15, 906–915 (2019). [Open Access URL]
Group(s): Ceriotti, Jaggi / Project(s): DD2
T. Würger, C. Feiler, F. Musil, G. B. V. Feldbauer, D. Höche, S. V. Lamaka, M. L. Zheludkevich, R. H. Meißner, Data Science Based Mg Corrosion Engineering, Frontiers in Materials 6, 53 (2019). [Open Access URL]
Group(s): Ceriotti / Project(s): DD2
B. Yin, W. A. Curtin, First-principles-based prediction of yield strength in the RhIrPdPtNiCu high entropy alloy, npj Computational Materials 5, 14 (2019). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
F. Maresca, D. Dragoni, G. Csányi, N. Marzari, W. A. Curtin, Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential, npj Computational Materials 4, 69 (2018). [Open Access URL]
Group(s): Curtin, Marzari / Project(s): DD2
M. J. Willatt, F. Musil, M. Ceriotti, Feature Optimization for Atomistic Machine Learning Yields a Data-Driven Construction of the Periodic Table of the Elements, Physical Chemistry Chemical Physics 20, 29661–29668 (2018). [Open Access URL]
Group(s): Ceriotti / Project(s): DD1, DD2