CPG
Tel: + 966 (013) 860-4984

Location: Bldg. 78, Rm. 1017

Dr. Christian Tantardini

Research Scientist III, CIPR

Educational Qualification

  • Ph.D., in Materials Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russian Federation – 2020
  • M.S., in Chemistry, University of Milan, Milan, Italy – Graduated with full marks and honors (cum laude) – 2014
  • B.S., in Chemistry and Industrial Chemistry (Chemical Science track), University of Insubria, Como, Italy – Graduated with full marks and merit in just 2 years – 2012.

Research Interests

  • Physical Chemistry
  • Materials Science
  • Solid State Physics
  • Machine Learning

Selected Publications

  • Gonze, S. Rostami, C. Tantardini. Variational Density Functional Perturbation Theory for Metals. Physical Review B, 109, 014317 (2024).
  • Tantardini, M. Iliaš, M. Giantomassi, A. G. Kvashnin, V. Pershina, X. Gonze. Generating and grading 34 optimised norm-conserving Vanderbilt pseudopotentials for actinides and super-heavy elements in the PseudoDojo. Computer Physics Communications, 295, 109002 (2024).
  • Tantardini, A. G Kvashnin, M. Azizi, X. Gonze, C. Gatti, T. Altalh, B. I. Yakobson. Electronic Properties of Functionalized Diamanes for Field Emission Display. ACS Applied Materials & Interface, 14 (40), 9118-9125 (2023).
  • Tantardini, A. Oganov. Thermochemical Electronegativities of the Elements. Nature Communications 12, 2087 (2021).
  • Tantardini, A. G Kvashnin, C. Gatti, B. I. Yakobson, X. Gonze. Computational Modeling of 2D Materials under High Pressure and Their Chemical Bonding: Silicene as Possible Field-Effect Transistor. ACS Nano, 15, 6861−6871 (2021).

Awards & Honors

  • 18.03.2025 Winner Humboldt Research Fellowship Programme for Experienced Researchers for the project titled: “Overcoming Challenges in Numerical Solutions of the Bethe-Salpeter Equation”.
    Description of the project: Explore analytical and numerical techniques with Artificial Intelligence (grid search, random search, gradient-based methods, and metaheuristics) to optimize kernel parameters of Bethe-Salpeter equation for improved accuracy and minimized approximation errors.
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