Research areas: Theoretical Chemistry, Physical Chemistry, Materials, Polymers & Nanoscience

Quantum mechanics is accurate for materials and molecules, but it is prohibitively expensive for realistic systems. We are interested in developing methods to extend the scope of atomistic simulations, in order to understand and predict materials properties that are hard to access. The group deploys and designs a combination of techniques encompassing machine learning, enhanced sampling, path-integral molecular dynamics, and free energy estimation. The systems of study include energy materials, aqueous systems, and matter under extreme conditions.