D&D2 - Metal Alloys
Group Leaders
Related publications (until January 2021)
- 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 - 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 - 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 - 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 - 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 - 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