Associate Professor at Technical University Munich, Germany
Prof. Alessio Gagliardi is an Associate Professor at the Technical University of Munich (TUM), where his research focuses on the development and application of numerical models for simulating nanostructured devices. His work spans multiple scales, from atomistic to continuum, and addresses next-generation solar cells (organic semiconductor, dye-sensitized, and perovskite solar cells), electrochemical systems (fuel cells, batteries), and advanced organic semiconductor materials. He integrates nanoscale methods such as Density Functional Theory (DFT) and Quantum Green Functions with mesoscale approaches like Kinetic Monte Carlo and macroscopic drift–diffusion/continuum models, thereby providing a complete multiscale framework for device simulation. He is also a key developer of TiberCAD and gDFTB software, widely used for device modeling. In recent years, his research has strongly expanded toward machine learning and deep learning in material science, particularly for accelerating multiscale simulations, structure-to-property prediction, and bridging experimental results with theoretical models. Prof. Gagliardi obtained his B.Sc. (1997–2000, cum laude) and M.Sc. (2000–2003, cum laude) in Telecommunication Engineering from the University of Rome “Tor Vergata,” followed by a Ph.D. in Physics (2004–2007) at Paderborn University, Germany, under the supervision of Prof. Thomas Frauenheim, focusing on theoretical modeling and simulation of electron–phonon scattering processes in molecular electronic devices. He continued with postdoctoral research at Bremen University (2007–2008) and later at the University of Rome “Tor Vergata” (2008–2014), working with Prof. Aldo Di Carlo on the simulation of organic and dye-sensitized solar cells. In 2014 he joined TUM as a Tenure-Track Assistant Professor, and since 2020 he has been serving as Associate Professor. He is a founder and board member (2024–present) of the Atomistic Modeling Center (TUM), and a core member of the Munich Data Science Institute (MDSI) and the Munich Institute for Integrated Materials, Energy and Process Engineering (MEP). He has been actively involved in the Nanoelectronic Institute (TUM) since 2014 and contributes to the TUM-Asia program with Singapore Institute of Technology (SIT) and Nanyang Technological University (NTU). Prof. Gagliardi has presented at nearly 70 international conferences, with 26 invited talks, and was nominated for the ENI Energy Frontiers Award (2024). His supervision record at TUM includes around 100 MSc/BSc theses, 7 completed PhD dissertations, 2 postdoctoral fellows, and currently 9 PhD students and 1 PostDoc. He has authored over 160 peer-reviewed publications, which have collectively received more than 7,500 citations, with an h-index of 43. His academic distinction also includes membership in the Cluster of Excellence Nanosystems Initiative Munich. His research continues to advance the frontiers of nanostructured device modeling, combining physics-based numerical simulations with data-driven machine learning approaches to enable predictive design of energy materials and electronic systems.
Profiles: Orcid|Google Scholar
Featured Publications
Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things
Tuning halide perovskite energy levels
Resonant Electron Heating and Molecular Phonon Cooling in Single Junctions
Optimizing the size of platinum nanoparticles for enhanced mass activity in the electrochemical oxygen reduction reaction
Incoherent Electron− Phonon Scattering in Octanethiols
Understanding the inelastic electron-tunneling spectra of alkanedithiols on gold
A priori method for propensity rules for inelastic electron tunneling spectroscopy of single-molecule conduction