Two of the most challenging problems at the intersection of electronic structure theory and molecular dynamics simulations are the accurate representation of intermolecular interactions and the development of reduced-scaling algorithms applicable to large systems. To some extent, these two problems are antithetical, since the accurate calculation of non-covalent interactions typically requires correlated, post-Hartree-Fock methods whose computational scaling with respect to system size precludes the application of these methods to large systems. I will describe our many-body molecular dynamics (MB-MD) methodology for aqueous systems that overcomes these limitations and enables computer simulations from the gas to the condensed phase, with chemical and spectroscopic accuracy. MB-MD is a unified molecular dynamics framework that combines many-body representations for potential energy, dipole moment, and polarizability surfaces, derived entirely from correlated electronic structure data using supervised learning techniques, with quantum dynamics methods that explicitly account for nuclear quantum effects. I will discuss the accuracy and predictive ability of the MBMD methodology in the context of molecular modeling of complex systems, including solutions, interfaces, and porous materials, with a particular focus on the relationships between structural/dynamical properties and vibrational spectra.
Professor Paesani's research group develops new theoretical methodologies and algorithms at the intersection of quantum chemistry, statistical mechanics, and computer science to predict the behavior of complex molecular systems at different length and time scales for applications in materials research, energy, environmental chemistry, and biophysics.