Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. We develop methods for tackling the many challenges that arise when coarse-graining, including reproducing relevant higher-order structural correlations as well as recovering the connection to the true underlying dynamics.
Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. We apply existing methods and develop new methods for extracting the essential features from molecular simulation data, including the construction of kinetic models and performing dimensionality reduction and clustering using deep learning. Applications include characterizing conformational dynamics of disordered proteins, diffusion kinetics in glassy liquids, and polymorphism in polymer crystallization.
The interactions of intrinsically disordered proteins (IDPs) play an important role in biological processes but present a number of fundamental challenges for computational modeling. While single chain conformational dynamics can be described by coarse-grained models with near-atomic resolution and specialized implicit solvent interactions, much simpler models are often adopted for investigating interactions between multiple IDPs. We apply sophisticated methods for multiscale modeling and simulation analysis to help probe the essential driving forces in these challenging, biologically-relevant disordered systems.