Paolo Restuccia

I am a Junior Assistant Professor at the Physics and Astronomy Department since 2022.

My background is in computational condensed matter physics. I have experience in the development by high-level programming and application of computational tools to describe the properties of materials and model their functioning in operando conditions. These tools are based on state-of-the-art materials-modeling methods including ab initio, classical, Green’s function, machine-learning-based molecular dynamics, and high throughput workflows. I applied multiscale approaches by linking different computational schemes to extend the size and time-scale of the simulated systems. Among the several applications, I focus on the study of surface and interface phenomena, such as adhesion, friction and corrosion at an atomistic scale. I have recently developed a machine-learning based approach to predict the adsorption and catalytic properties of molecule-surface systems from first-principles databases.