Welcome to the Calegari group website!
Our group develops new tools in machine learning to explore the thermodynamics and kinetics of chemical reactions using first-principles molecular simulations. Machine learning has expanded the time and length scale of first-principles simulations by orders of magnitude, allowing a proper statistical sampling of thermally-activated chemical reactions in condensed phase with atomic level resolution. We connect our simulations with experiments by direct calculation of experimental observables, such as vibrational spectroscopy, free energy differences and structure factor. Our goal is to use computer simulations to provide fundamental understanding of materials used to produce renewable energy, capture CO2 from air, and to promote efficient ion transport in fuel cells and batteries.