March 14th 

"Role of Interfaces and Electrostatics for Chemical Transformation"

Chemical transformations rarely occur in a single homogeneous aqueous phase, but instead occur in niches, crevices, and impurity sites at confining interfaces between two or more phases of gases, liquids or solids. The effects of interfaces on molecular properties are ubiquitously present across diverse fields spanning nanochemistry and chemical (bio)catalysis, environmental and energy sciences, geosciences, and functional materials. Fundamentally, interfaces can alter solvent and solution compositions and phases to reformulate the transition states and pathways of chemical reactions and underlying transport mechanisms. I will introduce new theoretical models and methods, and applications to examine interfacial problems for reactive chemistry, to characterize proton hopping mechanisms in anionic reverse micelles and recent hypotheses around microdroplet chemistry.

March 15th 

"Physics-Inspired Machine Learning Methods:  A Status Report on Predictive Chemistry"

The size of chemical space is vast. This makes application of first principles quantum mechanical and advanced statistical mechanics sampling methods to identify binding motifs, conformational equilibria, and reaction pathways extremely challenging, even when considering better physical models, algorithms, or future exascale computing paradigms. If we could develop new and robust machine learning approaches, ideally grounded in physical principles, we would be able to better tackle many fascinating but quite difficult chemical, biological, and materials systems. At present, the application of machine learning to (bio)chemistry is still in its infancy, and I will describe applications ranging from to potential energy surfaces and property predictions to chemical to biophysical systems to see where machine learning is having impact.