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

Project leader

EPFL, Lausanne

Michele Ceriotti

Deputy director

EPFL, Lausanne

Anatole von Lilienfeld

Project leader

UniBas, Basel

Related publications (until January 2023)

D. Marchand, W. A. Curtin, Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn, Physical Review Materials 6, 053803 (2022). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
V. L. Deringer, A. P. Bartók, N. Bernstein, D. M. Wilkins, M. Ceriotti, G. Csányi, Gaussian Process Regression for Materials and Molecules, Chemical Reviews 121, 10073–10141 (2021).
Group(s): Ceriotti / Project(s): DD2
G. Imbalzano, M. Ceriotti, Modeling the Ga/As binary system across temperatures and compositions from first principles, Physical Review Materials 5, 063804 (2021). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Ceriotti / Project(s): DD2
F. Giberti, G. A. Tribello, M. Ceriotti, Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps, Journal of Chemical Theory and Computation 17, 3292–3308 (2021).
Dataset on Materials Cloud.
Group(s): Ceriotti / Project(s): DD2
N. Lopanitsyna, C. B. Mahmoud, M. Ceriotti, Finite-temperature materials modeling from the quantum nuclei to the hot electron regime, Physical Review Materials 5, 043802 (2021).
Dataset on Materials Cloud.
Group(s): Ceriotti / Project(s): DD2
F. Musil, M. Veit, A. Goscinski, G. Fraux, M. J. Willatt, M. Stricker, T. Junge, M. Ceriotti, Efficient implementation of atom-density representations, The Journal of Chemical Physics 154, 114109 (2021).
Group(s): Ceriotti, Curtin / Project(s): DD2
G. Imbalzano, Y. Zhuang, V. Kapil, K. Rossi, E. A. Engel, F. Grasselli, M. Ceriotti, Uncertainty estimation for molecular dynamics and sampling, The Journal of Chemical Physics 154, 074102 (2021). [Open Access URL]
Group(s): Ceriotti / Project(s): DD2
Y. Hu, W. A. Curtin, Modeling peak-aged precipitate strengthening in Al–Mg–Si alloys, Journal of the Mechanics and Physics of Solids 151, 104378 (2021). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
A. C. P. Jain, D. Marchand, A. Glensk, M. Ceriotti, W. A. Curtin, Machine learning for metallurgy III: A neural network potential for Al-Mg-Si, Physical Review Materials 5, 053805 (2021). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Ceriotti, Curtin / Project(s): DD2
V. L. Deringer, N. Bernstein, G. Csányi, C. Ben Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, S. R. Elliott, Origins of structural and electronic transitions in disordered silicon, Nature 589, 59–64 (2021). [Open Access URL]
Dataset on Zenodo.
Group(s): Ceriotti / Project(s): DD2
B. Yin, S. Yoshida, N. Tsuji, W. A. Curtin, Yield strength and misfit volumes of NiCoCr and implications for short-range-order, Nature Communications 11, 2507 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
M. Stricker, B. Yin, E. Mak, W. A. Curtin, Machine learning for metallurgy II. A neural-network potential for magnesium, Physical Review Materials 4, 103602 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
J. Nigam, S. Pozdnyakov, M. Ceriotti, Recursive evaluation and iterative contraction of N-body equivariant features, The Journal of Chemical Physics 153, 121101 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Ceriotti / Project(s): DD2
M. Zamani, G. Imbalzano, N. Tappy, D. T. L. Alexander, S. Martí-Sánchez, L. Ghisalberti, Q. M. Ramasse, M. Friedl, G. Tütüncüoglu, L. Francaviglia, S. Bienvenue, C. Hébert, J. Arbiol, M. Ceriotti, A. F. i. Morral, 3D Ordering at the Liquid-Solid Polar Interface of Nanowires, Advanced Materials 32, 2001030 (2020). [Open Access URL]
Group(s): Ceriotti / Project(s): DD2
D. Marchand, A. Jain, A. Glensk, W. A. Curtin, Machine learning for metallurgy I. A neural-network potential for Al-Cu, Physical Review Materials 4, 103601 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
M. Stricker, W. A. Curtin, Prismatic Slip in Magnesium, The Journal of Physical Chemistry C 124, 27230 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
B. Yin, F. Maresca, W. A. Curtin, Vanadium is an optimal element for strengthening in both fcc and bcc high-entropy alloys, Acta Materialia 188, 486 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
B. Yin, W. A. Curtin, Origin of high strength in the CoCrFeNiPd high-entropy alloy, Materials Research Letters 8, 209 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Curtin / Project(s): DD2
Y. Hu, B. A. Szajewski, D. Rodney, W. A. Curtin, Atomistic dislocation core energies and calibration of non-singular discrete dislocation dynamics, Modelling and Simulation in Materials Science and Engineering 28, 015005 (2020). [Open Access URL]
Group(s): Curtin / 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
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
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
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. 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