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2020

  1. Stopka K.S. and McDowell, D.L., “Microstructure-sensitive computational estimates of driving forces for surface vs. subsurface fatigue crack formation in duplex Ti-6Al-4V and Al 7075-T6,” JOM, Vol. 72, No. 1, 2020, pp. 28-38.
  2. Stopka, K.S. and McDowell, D.L., “Microstructure-Sensitive Computational Multiaxial Fatigue of Al 7075-T6 and Duplex Ti-6Al-4V,” International Journal of Fatigue, Vol. 133, 2020, p. 105460.
  3. Gu, T., Stopka, K.S., Xu, C., and McDowell, D.L., “Prediction of maximum Fatigue Indicator Parameters for duplex Ti-6Al-4V using extreme value theory,” Acta Materialia, Vol. 188, 2020, pp. 504-516.
  4. Zhang, Y., Chen, W., McDowell, D.L., Wang, Y.M., and Zhu, T., “Lattice Strains and Diffraction Elastic Constants of Cubic Polycrystals,” Journal of the Mechanics and Physics of Solids, Vol. 138, 2020, p. 103899.
  5. Tallman, A.E., Swiler, L.P., Wang, Y., and McDowell, D.L., “Uncertainty Propagation in Reduced Order Models based on Crystal Plasticity,” Computer Methods in Applied Mechanics and Engineering, Vol. 365, 2020, p. 113009.
  6. Stopka, K.S., Gu, T., and McDowell, D.L., “Effects of Algorithmic Simulation Parameters on the Prediction of Extreme Value Fatigue Indicator Parameters in Duplex Ti-6Al-4V,”  Int. J. Fatigue, Vol. 141, 2020, p. 105865.
  7. Gupta, A., Gruber, J., Rajaram, S.R., Thompson, G.B., McDowell, D.L., and Tucker, G.J., “On the Mechanistic Origins of Maximum Strength in Nanocrystalline Metals: Attaining Gridlock at the Nanoscale,” npj Computational Materials, 6, 2020, 153; https://doi.org/10.1038/s41524-020-00425-0.
  8. Chu, K., Foster, M.E., Sills, R.B., Zhou, X., Zhu, T., and McDowell, D.L., “Temperature and Composition Dependent Mobility of Screw Dislocations in Fe0.7NixCr0.3-x Austenitic Stainless Steels from Large Scale Molecular Dynamics Simulationa,” npj Computational Materials 6, 2020, 179; https://doi.org/10.1038/s41524-020-00452-x.
  9. McDowell, D.L., “Gaps and Barriers to the Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows,“ JOM, Vol. 73, 2020, pp. 138-148.
  10. Whelan, G. and McDowell, D.L., “Machine Learning Enabled Uncertainty Quantification for Modeling Fatigue Critical Engineering Alloys Using an ICME Workflow,” Integr Mater Manuf Innov, TMS, Vol. 9, 2020, pp. 376-393.