Marco Bertani

Background

About Marco Bertani

After the master's degree in chemical sciences where I approached glass science (master thesis), I received a scholarship from the University of Modena and Reggio Emilia focused on developing empirical force fields for molecular dynamics. This work continued during my Ph.D. when I conducted research focused on the exploitation of Machine Learning techniques in the computational simulation of glasses. This resulted in three main projects about the prediction of NMR isotropic shielding using Kernel Ridge Regression (in collaboration with CEA Paris Saclay), parameterization of empirical interatomic potentials using Bayesian Optimization, and generation of an ML-potential using DeePMD (Neural Networks). All the projects were published in international peer-reviewed journals. Currently, I am working, with a Post-Doc position at the University of Modena and Reggio Emilia, on the simulation of sodium-based amorphous electrolytes for All-Solid-State batteries of the next generation with reduced environmental impact. My expertise is molecular dynamics simulations, force field development, machine learning applied to properties prediction, and structural analysis. I also have a background in experimental investigation, especially using solid-state NMR spectroscopy. In the last years, I published several papers, collaborated with international groups (CEA Paris Saclay, Rutger University, ACG Japan.), and participated in international congresses (GOMD 2022/2023/2024, ICG 2022, SCI2022.)