One of the biggest events sponsored by the Cornell local section of the ACS is the Debye lecture series, which is also one of the most prestigious lecture series offered by the Cornell Department of Chemistry and Chemical Biology. We are happy to welcome Teresa Head-Gordon, Chancellor’s Professor of Chemistry, Bioengineering, and Chemical and Biomolecular Engineering at UC Berkeley. She specializes in computation and theory in the areas of chemistry, materials, and biophysics. She is coming to give TWO lectures, one tonight (March 14) and the other tomorrow (March 15). These lectures will take place in Baker lab, room 119, on the Cornell Campus, but will ALSO be available to local section members via Zoom. You should receive an email with the relevant links. If you do not, and would like to watch the lectures, please email the secretary, wpk8@cornell.edu. We apologize for the very short notice here, but on the bright side we ALSO hope to make the lectures available after the fact, and will make those available as widely as possible if we are able.
“Role of Interfaces and Electrostatics for Chemical Transformation”
March 14, 2024, 4:00 PM
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.
“Physics-Inspired Machine Learning Methods: A Status Report on Predictive Chemistry”
March 15, 2024, 4:00 PM
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.