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Home » Institute » Departments & Research Groups » Atomistic Modelling and Simulation » Data-Driven Methods for Atomistic Simulations » DDMA Publications

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  • A. Ferrari, M. Schröder, Y. Lysogorskiy et al. Phase transitions in titanium with an analytic bond-order potential. Modelling and Simulation in Materials Science and Engineering, 27, 085008, (2019)
  • C. Sutton, L. M. Ghiringhelli, T. Yamamoto et al. Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition. npj Computational Materials, 5, 111, (2019)
  • S. Menon, G. Díaz Leines, J. Rogal. Pyscal: a Python module for structural analysis of atomic environments. Journal of Open Source Software, 4, 1824, (2019)
  • J. Janssen, S. Surendralal, Y. Lysogorskiy et al. Pyiron: an integrated development environment for computational materials science. Computational Materials Science, 163, 24-36, (2019)
  • A. Ferrari, P. Kadletz, T. Chakraborty et al. Reconciling experimental and theoretical data in the structural analysis of Ti-Ta shape memory alloys. Shape Memory and Superelasticity, 5, 6-15, (2019)
  • T. Hammerschmidt, B. Seiser, M. Ford et al. BOPfox program for tight-binding and analytic bond-order potential calculations. Computer Physics Communications, 235, 221-233, (2019)
  • Y. Lysogorskiy, T. Hammerschmidt, J. Janssen et al. Transferability of interatomic potentials for molybdenum and silicon. Modelling and Simulation in Materials Science and Engineering, 27, 025007, (2019)
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