DD2

Published datasets resulting from Design and Discovery project DD2 "From Atoms to Additive Manufacturing: Computational Design of Complex Metal Alloys".

10.24435/materialscloud:2c-7c — Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys, by Y. Hu, W. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:2k-cy — DFT data for giant hardening response in AlMgZn(Cu) alloys, by D. Marchand, C. William
Related MARVEL publication:
- L. Stemper, M. A. Tunes, P. Dumitraschkewitz, F. Mendez-Martin, R. Tosone, D. Marchand, W. A. Curtin, P. J. Uggowitzer, S. Pogatscher, Giant hardening response in AlMgZn(Cu) alloys, Acta Materialia 206, 116617 (2021). [Open Access URL]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:tb-dq — Modeling the Ga/As binary system across temperatures and compositions from first principles, by G. Imbalzano, M. Ceriotti
Related MARVEL publication:
- 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]
  Group(s): Ceriotti / Project(s): DD2
10.24435/materialscloud:vk-qd — Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime, by N. Lopanitsyna, C. Ben Mahmoud, M. Ceriotti
Related MARVEL publication:
- 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).
  Group(s): Ceriotti / Project(s): DD2
10.24435/materialscloud:py-h3 — Global free-energy landscapes as a smoothly joined collection of local maps, by F. Giberti, G. Tribello, M. Ceriotti
Related MARVEL publication:
- 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).
  Group(s): Ceriotti / Project(s): DD2
10.24435/materialscloud:k1-rv — Machine learning for metallurgy: a neural network potential for Al-Mg-Si, by A. C. P. Jain, D. Marchand, A. Glensk, M. Ceriotti, W. A. Curtin
Related MARVEL publication:
- 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]
  Group(s): Ceriotti, Curtin / Project(s): DD2
10.24435/materialscloud:s4-g3 — Yield strength and misfit volumes of NiCoCr and implications for short-range-order, by B. Yin, W. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2
10.5281/zenodo.4174139 — Research data for "Origins of structural and electronic transitions in disordered silicon", by V. L. Deringer, N. Bernstein, G. Csányi, C. B. Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, S. R. Elliott
Related MARVEL publication:
- 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]
  Group(s): Ceriotti / Project(s): DD2
10.24435/materialscloud:8f-1s — Pure Magnesium DFT calculations for interatomic potential fitting, by B. Yin, M. Stricker, W. A. Curtin
Related MARVEL publications:
- M. Stricker, W. A. Curtin, Prismatic Slip in Magnesium, The Journal of Physical Chemistry C 124, 27230 (2020). [Open Access URL]
  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]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:6k-je — Machine learning for metallurgy: a neural network potential for Al-Cu, by D. Marchand, A. Jain, A. Glensk, W. A. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:qy-dp — Randomly-displaced methane configurations, by S. Pozdnyakov, M. Willatt, M. Ceriotti
Related MARVEL publication:
- 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]
  Group(s): Ceriotti / Project(s): DD2
10.24435/materialscloud:2020.0045/v1 — Origin of high strength in the CoCrFeNiPd high-entropy alloy, by B. Yin, W. A. Curtin
Related MARVEL publication:
- B. Yin, W. A. Curtin, Origin of high strength in the CoCrFeNiPd high-entropy alloy, Materials Research Letters 8, 209 (2020). [Open Access URL]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:2020.0020/v1 — Vanadium is an optimal element for strengthening in both fcc and bcc high-entropy alloys, by B. Yin, F. Maresca, W. A. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:2019.0089/v1 — Stress-dependence of generalized stacking fault energies: a DFT study, by B. Yin, P. Andric, W. A. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2
10.24435/materialscloud:2018.0019/v1 — Special quasi-random structures for the 6-component high entropy alloys, by B. Yin, W. Curtin
Related MARVEL publication:
- 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]
  Group(s): Curtin / Project(s): DD2