Computational materials science, ‎0927-0256

Journal

ISSNs0927-0256
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Research output

  1. Published

    Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride

    Nayak, G. K., Srinivasan, P., Todt, J., Daniel, R., Nicolini, P. & Holec, D., 6 Jan 2025, In: Computational materials science. 249.2025, 5 February, 11 p., 113629.

    Research output: Contribution to journal › Article › Research › peer-review

  2. Published

    A micromechanical approach to constitutive equations for phase changing materials

    Fischer, F. D., Oberaigner, E. & Tanaka, K., 1997, In: Computational materials science. 9, p. 56-63

    Research output: Contribution to journal › Article › Research › peer-review

  3. Published

    An atomistic view on Oxygen, antisites and vacancies in the γ-TiAl phase

    Razumovskiy, V. I., Ecker, W., Wimler, D., Fischer, F.-D., Appel, F., Mayer, S. & Clemens, H., Sept 2021, In: Computational materials science. 197.2021, September, 8 p., 110655.

    Research output: Contribution to journal › Article › Research › peer-review

  4. Published

    A New Computational Treatment of Reactive Diffusion in Binary Systems

    Svoboda, J. & Fischer, F. D., 2013, In: Computational materials science. 78, p. 39-46

    Research output: Contribution to journal › Article › Research › peer-review

  5. Published

    Application of the cyclic phase transformation concept for determining the effective austenite/ferrite interface mobility

    Gamsjäger, E., Chen, H. & van der Zwaag, S., 2014, In: Computational materials science. 83, p. 92-100

    Research output: Contribution to journal › Article › Research › peer-review

  6. Published

    Austenite-to-Ferrite Phase Transformation in Low-Alloyed Steels

    Gamsjäger, E., Svoboda, J. & Fischer, F. D., 2005, In: Computational materials science. 32, p. 360-369

    Research output: Contribution to journal › Article › Research › peer-review

  7. Published

    Back to and beyond Weibull – The hazard rate approach

    Stoyan, D., Funke, C. & Rasche, S., 2013, In: Computational materials science. 68, p. 181-188

    Research output: Contribution to journal › Article › Research › peer-review

  8. Published

    Coupled Modelling of the Solidification Process Predicting Temperatures, Stresses and Microstructures

    Ludwig, A. & Sahm, P., 1996, In: Computational materials science. p. 194-198

    Research output: Contribution to journal › Article › Research › peer-review

  9. Published

    Data-mining of in-situ TEM experiments: Towards understanding nanoscale fracture

    Steinberger, D., Issa, I., Strobl, R., Imrich, P. J., Kiener, D. & Sandfeld, S., 5 Jan 2023, In: Computational materials science. 216.2023, 5 January, 9 p., 111830.

    Research output: Contribution to journal › Article › Research › peer-review

  10. Published

    Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies

    Dösinger, C. A., Hammerschmidt, T., Peil, O. E., Scheiber, D. & Romaner, L., 13 Nov 2024, In: Computational materials science. 247.2025, 31 January 2025, 10 p., 113493.

    Research output: Contribution to journal › Article › Research › peer-review

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