Like most scientists, chemists are drowning in data from laboratory experiments and from calculations. We are developing tools using machine learning to automate the analysis of quantum-chemistry. Another area in need of automation is in the development of quantitative structure-property relationships, particularly where flexible molecules are concerned.
The discovery of new catalysts drives chemistry forwards, yet this is still dependent on trial-and-error experimentation. Screening large numbers of molecules, additives and solvent systems is inefficient, costly and wasteful. We explore computational approaches to understand and explore structure, mechanism and selectivity in catalytic transformations.
Elucidating enzymatic mechanisms helps to explain how enzymes work and predict their behavior. Through experimental collaborations, classical and quantum modeling we explore this avenue in efforts to reveal the mechanistic underpinnings of important enzymatic reactions. Along the way, we aim to expand conceptual and methodological foundations.
