Computational materials science, ‎0927-0256

Journal

ISSNs0927-0256

Research output

  1. 2025
  2. 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 2025, In: Computational materials science. 247.2024, 31 January, 10 p.

    Research output: Contribution to journalArticleResearchpeer-review

  3. 2023
  4. 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 journalArticleResearchpeer-review

  5. 2022
  6. E-pub ahead of print

    Stability and ordering of bcc and hcp TiAl+Mo phases: An ab initio study

    Dehghani, M., Ruban, A. V., Abdoshahi, N., Holec, D. & Spitaler, J., 18 Jan 2022, (E-pub ahead of print) In: Computational materials science. 205.2022, 1 April, 11 p., 111163.

    Research output: Contribution to journalArticleResearchpeer-review

  7. 2021
  8. 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 journalArticleResearchpeer-review

  9. 2018
  10. Published

    Designing nanoindentation simulation studies by appropriate indenter choices: Case study on single crystal tungsten

    Goel, S., Cross, G., Stukowski, A., Gamsjäger, E., Beake, B. & Agrawal, A., 2018, In: Computational materials science. 152, p. 196-210 15 p.

    Research output: Contribution to journalArticleResearchpeer-review

  11. 2017
  12. E-pub ahead of print

    Structure and surface energy of Au55 nanoparticles: An ab initio study

    Holec, D., Fischer, F-D. & Vollath, D., 6 Apr 2017, (E-pub ahead of print) In: Computational materials science. 134.2017, 15 June, p. 137-144 8 p.

    Research output: Contribution to journalArticleResearchpeer-review

  13. Published

    Incorporation of vacancy generation/annihilation into reactive diffusion concept – Prediction of possible Kirkendall porosity

    Svoboda, J. & Fischer, F-D., 1 Feb 2017, In: Computational materials science. 127.2017, 1 February, p. 136-140 5 p.

    Research output: Contribution to journalArticleResearchpeer-review

  14. 2016
  15. Published
  16. Published
  17. Published

    Predicting an alloying strategy for improving fracture toughness of C15 NbCr2 Laves phase: A first-principles study

    Long, Q., Wang, J., Du, Y., Holec, D., Nie, X. & Jin, Z., 2016, In: Computational materials science. 123, p. 59-64 6 p.

    Research output: Contribution to journalArticleResearchpeer-review

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