BIOGRAPHY

Prof. Gagliardi research is on the development and application of numerical models for the simulation of nanostructured devices. His focus is on new solar cells (organic semiconductors based on perovskite), electrochemical systems (fuel cells, batteries) and organic semiconductor materials. The development of new models ranges from the nanoscale (Density Functional Theory, Quantum Green Functions) through the mesoscale (Kinetic Monte Carlo) to the macroscopic scale (drift diffusion, continuum models). He is also a developer of TiberCAD and the GDFTB software. His latest research is on multiscale modeling for organic semiconductors and the use of machine/deep learning approaches in material science.

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 postdoc at the Bremen Center for Computing Materials and in Rome before being appointed Tenure Track Assistant Professor at TUM in 2014. Since 2020 he is Associate Professor at TUM.

RESEARCH INTERESTS

• Molecular electronics, charge transport and dissipation in nanoscale devices
• Many-body corrections (GW, TDDFT) to simulate transport in quantum systems
• Photovoltaic devices (Dye Solar cells, polymeric cells, small molecule cells)
• Organic memories (resistive bistable memories)
• Connection between information theory and statistical mechanics
• Smart grids

KEY PUBLICATIONS

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. 2020; 11:2895-2906.
   Abstract
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. 2019; 58, 9596-9600.
   Abstract
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. 2020; 124: 17733-17743.
   Abstract
4. 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. 2020; 11: 1773-1780.
   Abstract
5. 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. 2019; 10 (20): 6097-61041.
   Abstract