I am a Postdoctoral Research Associate at the Chemistry Department. I am a member of the Computational Materials Science Group of Prof. Nicholas Harrison. My main activity is the study of corrosion by means of ab initio calculation.
In March 2017, I completed cum laude a PhD in "Multiscale modelling of tribological systems: adsorbed monolayer and carbon-based materials" at the University of Modena. Then I worked for two years as PostDoc in the Computational Tribology Group in the same University. I started my current work at Imperial College in February 2019.
My research career focuses on studying chemical and physical reactions occurring at surfaces and interfaces, specifically in the study of corrosive and tribological processes. I have performed these analyses with state-of-the-arts computational materials science approaches, namely static and dynamics first-principles simulations based on density functional theory, classical Molecular Dynamics and Quantum Mechanics/Molecular Mechanics multi-scale approach. I have also developed automated workflows for high-throughput calculations to study relevant figures of merit in the adhesion and sliding of homogenous interfaces and molecular dissociation of molecules over substrates.
Benini F, Restuccia P, Righi MC, 2024, Zinc dialkyldithiophosphates adsorption and dissociation on ferrous substrates: An ab initio study, Applied Surface Science, Vol:642, ISSN:0169-4332
et al., 2023, Modeling phosphorene and MoS<sub>2</sub> interacting with iron: lubricating effects compared to graphene, Journal of Nanostructure in Chemistry, Vol:13, ISSN:2008-9244, Pages:497-505
Pedretti E, Restuccia P, Righi MC, 2023, Xsorb: A software for identifying the most stable adsorption configuration and energy of a molecule on a crystal surface, Computer Physics Communications, Vol:291, ISSN:0010-4655
et al., 2023, High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts, Acs Applied Materials & Interfaces, Vol:15, ISSN:1944-8244, Pages:19624-19633
et al., 2023, Doping carbon electrodes with sulfur achieves reversible sodium ion storage, Journal of Physics-energy, Vol:5, ISSN:2515-7655