Michele Reticcioli

Michele Reticcioli is a researcher in quantum and computational materials physics, currently affiliated with CNR L'Aquila, Italy, after previously serving at the University of Vienna until 2024. His work focuses on polarons, surface science, and machine learning applications in materials discovery. He leads research groups exploring electronic properties of materials using advanced computational methods like density functional theory (DFT) and has contributed to understanding polaron dynamics, surface reconstructions, and catalytic mechanisms on oxide surfaces.

Teaching includes courses on computational quantum mechanics, data science for physicists, and surface science at the University of Vienna, emphasizing practical simulations using tools like VASP. He has supervised numerous students in topics like machine learning for polaron modeling, surface defect analysis, and computational materials physics.

His research has been published in top journals such as Nature Communications, Science Advances, and Physical Review Letters, with a focus on interdisciplinary approaches combining theory and experiments. Key areas include polaron-driven surface phenomena, thermoelectric materials, and superconductivity in hydrides.