Coarse-graining Interactions

About This Grant

William Noid of the Pennsylvania State University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop improved methods for coarse-grained (CG) models of polymers and peptides. By averaging over unnecessary details, CG models provide the necessary computational efficiency for investigating polymeric properties and biological processes on length- and time-scales that are not accessible to atomistic models. However, existing CG models generally provide limited accuracy and also unpredictable transferability, i.e., they may require re-parameterization for each system of interest. This unpredictable transferability significantly reduces the computational advantages of CG models. Accordingly, William Noid and his research group will develop theory and computational methods for improving the accuracy and transferability of CG models. These advances will enhance the national research infrastructure for studying biological processes and for developing improved polymeric materials. Additionally, William Noid will continue developing an intergenerational science club that bridges the academic and civic communities. William Noid and his research group will initially investigate metrics for identifying optimal CG representations for polymers and peptides. Noid and his group will then leverage rigorous analysis of the exact many-body potential of mean force (PMF) to develop methods for accurately describing structural, thermodynamic, and dynamic properties. In particular, Noid and his group will develop a data-efficient local energy-matching variational principle to optimally approximate the energetic contribution to the PMF. Noid and his group will develop global density potentials for describing the pressure equation of state, as well as local density potentials for describing many-body hydrophobic interactions. Moreover, Noid and his group will employ the generalized-Yvon-Born-Green framework and rigorous variational principles to accurately model the free energy surface for complex systems that transition between multiple conformation states. Additionally, Noid and his group will explore an exact decomposition of the time evolution operator for modeling dynamical properties. Finally, Noid will provide students with rigorous, interdisciplinary training in modern statistical mechanics and will continue developing an outreach program that promotes scientific literacy and a healthy lifestyle of lifelong learning among local seniors. 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.