M. Scott Shell of the University of California Santa Barbara is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Chemistry Division to develop broad-based algorithms for systematic coarse-graining. Computer simulations of the molecular world are an established and invaluable tool for understanding how the concert of atomic motions and interactions gives rise to observed properties in biology, materials, fluid mixtures, etc. Simulations may also suggest novel experimental directions for the design of new synthetic molecules. It is very computationally expensive to model every single atom in a molecule over even the shortest times (millionths of a second). This expense puts severe limits on the complexity of simulations that can be pursued. Dr. Shell and coworkers have developed an approach by which unnecessary atomic detail in simulations can be automatically identified. These unnecessary details are replaced by 'coarse-grained' models that require far fewer calculations. In turn, these coarse models permit simulations on dramatically larger scales. This capability opens new problems and systems of study. This project develops this basic approach by creating powerful ways to maintain high accuracy in highly coarse models and applies the new technique to understand peptide-based materials. This project provides educational opportunities for students at multiple levels, including involvement of those from underrepresented groups. Professor Shell mentors undergraduate researchers and participates in several campus-wide diversity initiatives that contribute to the professional development and research training programs at UCSB's California Nanosystems Institute. The team develops strategies to improve the recruitment of women students in theoretical and computational research. A yearly seminar coordinated by Dr. Shell brings distinguished female researchers to campus to serve as role models for undergraduates, graduate students, and postdocs.
While coarse-graining methods have been vigorously pursued in recent years, major practical and conceptual barriers remain that compromise coarse-grained (CG) model accuracy, transferability, and automation, and thus limit the general reliability of these all-important modeling approaches. This project leverages the relative entropy framework developed by the Shell group to create general new strategies for addressing these limitations. These new methods are rooted in rigorous statistical mechanical theory and state-of-the-art molecular simulation techniques. Specifically, the project creates robust strategies for efficiently and accurately modeling complex, multibody CG interactions across state conditions, chemistries, and systems, and for seamlessly including external constraints (like experimental information) in the bottom-up CG strategy. As an application of the techniques, this work develops a next-generation, general but sequence-sensitive CG peptide model suitable for understanding and predicting supramolecular self-assembly in emerging hybrid polymer-peptide materials.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
