Chemistry is all around us
It impacts everything we can see, touch, smell
Our faculty provide teaching and research expertise in all major areas of Chemistry
Please Note: Our grateful thanks to Dr and Mrs Victor Garsky who have donated $10,000 to the Department in acknowledgement of the help and guidance given to Dr Garsky by Emeritus Professors Gerard V. Smith and David F. Koster. The gift will be used as financial support for part-time employment of undergraduate students with the expressed goal of giving these students laboratory research experience.
The Department offers weekly seminars by faculty from departments throughout the United States and beyond describing the latest advances in their fields.
Departmental Seminars are held in the Van Lente Auditorium (Neckers 240) at 4:00 pm unless otherwise indicated below.
Professor Andrew Ferguson, Institute for Molecular Engineering, University of Chicago, Friday, January 18, 2019
" Machine Learning and Data Science for Understanding and Design in Colloidal Assembly and Protein Folding "
Abstract: Data-driven modeling and machine learning have opened new paradigms and opportunities in the understanding and design of soft and biological materials. The automated discovery of emergent collective variables within high-dimensional computational and experimental data sets provides a means to understand and predict materials behavior and engineer properties and function. I will describe our recent work in the use of two machine learning techniques for collective variable discovery within molecular simulation – nonlinear manifold learning using diffusion maps, and nonlinear dimensionality reduction using autoencoding neural networks (“autoencoders”). First, I will describe our applications of graph matching and diffusion maps to determine low-dimensional assembly landscapes for self-assembling patchy colloids. These landscapes connect colloid architecture and prevailing conditions with emergent assembly behavior, and we use them to perform inverse building block design by rationally sculpting the landscape to engineer the stability and accessibility of desired aggregates. Second, I will describe our use of autoencoders to perform automated discovery of collective variables in protein folding. We interleave deep learning variable discovery and enhanced sampling directly within the discovered variables to perform simultaneous on-the-fly variable discovery and accelerated sampling of protein folding funnels.
Materials Technology Center Seminar Series