Interessi di ricerca

Atomistic simulations based on high-accuracy DFT calculations offer the opportunity to gain an increasingly deeper understanding of the structural and electronic properties of MXenes, materials whose potential is still largely unexplored. The data generated from these studies are used to train a Machine Learning Interatomic Potential (MLIP), aiming to efficiently and predictively capture energy landscapes and atomic dynamics. This approach paves the way for exploring complex scenarios and time scales that remain challenging for traditional quantum-mechanical methods.

Parole chiave: Ab Initio Molecular Dynamics, Machine learning potential, DFT, MXenes

Affiliazioni: SCI