Prof. Dr. Alessio Gagliardi





Professor Gagliardi’s research focuses on the development and application of numerical models to simulate nanostructured devices, in particular, new solar cells (based on organic semiconductors or perovskite materials), electrochemical systems (nanostructured cathodes for fuel cells) and organic semiconductor materials for electronic applications. The development and application of numerical models spans from the nanoscale (Density Functional Theory, Quantum Green functions) to the mesoscale (Kinetic Monte Carlo) and the macroscale (Drift-diffusion). He is also a developer of the TiberCAD and GDFTB software. His most recent research topic focuses on multiscale modeling and the application of machine learning methods for material property screening.

After studying engineering at the University of Rome Tor Vergata (Italy), Professor Gagliardi received his doctorate in physics from the University of Paderborn in 2007. He later worked as a postdoctoral researcher at the Bremen Center for Computational Material Science and in Rome, before being appointed tenure track assistant professor at TUM in 2014. Since 2020 he is an Associate Professor at TUM.


Finite element based semi-classical transport modeling (development and applications)

Kinetic Monte Carlo (development and applications)

Atomistic modeling and quantum transport (development and application)

Nanostructured photovoltaic devices


Thermodynamics at the nanoscale

Machine learning

Electrochemical systems


  1. H Michaels, M Rinderle, R Freitag, I Benesperi, T Edvinsson, R Socher, A Gagliardi, M Freitag; Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things, CHEMICAL SCIENCE, 11, 2895-2906 (2020).
  2. B Garlyyev, K Kratzl, M Rück, J Michalička, J Fichtner, J M. Macak, T Kratky, S Günther, M Cokoja, A Bandarenka, A Gagliardi, R. A. Fischer; How small: selecting the optimal size of Pt nanoparticles for enhanced oxygen electro-reduction mass activity, Angewandte Chemie, 58, 9596-9600 (2019).
  3. M Rinderle, W Kaiser, A Mattoni, A Gagliardi; Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films, Journal of Physical Chemistry C, 124, 17733-17743 (2020).
  4. M Rück, A Bandarenka, F Calle-Vallejo, A Gagliardi, Oxygen Reduction Reaction: Rapid Prediction of Mass Activity of Nanostructured Platinum Electrocatalysts, The journal of physical chemistry letters, 9 (15), 4463-4468 (2018).
  5. M Rück, A Bandarenka, F Calle-Vallejo, A Gagliardi; Fast Identification of Optimal Pure Platinum Nanoparticle Shapes and Sizes for Efficient Oxygen Electroreduction, Nanoscale Advances 1 (8), 2901-2909 (2019).
  6. M Rück, B Garlyyev, F Mayr, A S Bandarenka, A Gagliardi; Oxygen Reduction Activities of Strained Platinum Core–Shell Electrocatalysts Predicted by Machine Learning, Journal of physical chemistry letters, 11, 1773-1780 (2020).
  7. W Kaiser, A Gagliardi; Kinetic Monte Carlo Study of the Role of the Energetic Disorder on the Open-Circuit Voltage in Polymer/Fullerene Solar Cells, Journal of physical chemistry letters, 10 (20), 6097-61041 (2019).
  8. J Lederer, W Kaiser, A Mattoni, A Gagliardi, Machine Learning–Based Charge Transport Computation for Pentacene, Adv. Theory Simul., 180013 (2018).
  9. Gagliardi A.; Abate A.; Mesoporous Electron Selective Contacts Enhance the Tolerance to Interfacial Ions Accumulation in Perovskite Solar Cells; ACS Energy Letters 12 1-20 (2017).
  10. Gagliardi A.; Auf der Maur M.; Gentilini D.; Di Fonzo F.; Abrusci A.; Snaith H.J.; Divitini G.; Ducati C.; Di Carlo A.; The real TiO2/HTM interface of solid-state dye solar cells: role of trapped states from a multiscale modelling perspective Nanoscale; Nanoscale 7 1136 (2015).

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