The group performs simulations, experiments, and statistical learning on a wide variety of molecular and solid-state materials. Modelling and experimentation often go hand-in-hand - the former helps us “see” information at the level of detail often not attainable by experiments and can serve as a powerful tool for predicting novel materials or providing experimental support, while the latter is indispensable for realizing theoretical predictions or producing real materials with practical applications. The results from modelling and experimentation are then coupled with statistical learning to accelerate the discovery of novel molecules and materials.
Our modelling projects include applying electronic structure methods towards elucidating structural-property relationships, predicting structures and reaction pathways, finding design principles, building datasets, and doing statistical learning on one or more of the following (but the list is always expanding!)
Our experimental projects involve using automated synthesis/characterization to build experimental datasets and applying statistical learning techniques for accelerating discoveries of novel materials. These involve at least some of the materials involved in the modelling projects above, but we also have some projects that lean more towards the experimental side without electronic structure modelling, which include (again, the list can always expand!):