Computational Hybrid Soft Materials Laboratory

Biomolecular Materials

We are very interested in scientific problems in life and biomedical sciences and engineering, with specific focus on Biomolecular Materials. Biomolecular materials typically encompass synthetic and natural polymers such as peptides, proteins, nucleic acids, and bioinspired polymers. The problems include therapeutic encapsulation and delivery, electronics, synthetic cells, and programmable self-assembly. Systems of interest include: dendronized vesicles for delivery of charged biomolecules; formation of biological materials for electronics; preservation of structure-function properties of biomolecules; and formation of hybrid assemblies encompassing isotropic and anisotropic building blocks. These investigations include collaborative efforts with groups at Rutgers University, Princeton University, and Brookhaven National Laboratory. See figure on the right.

Multiscale Molecular and Materials Modeling

The design of biomolecular materials requires resolving physical phenomena over multiple spatiotemporal scales. Our lab supports this effort via the development and use of multiscale molecular and materials methods, models, and characterization techniques. The multiscale methods include the Molecular Dynamics-Lattice Boltzmann technique which couples a discrete fluid technique with a particle dynamics technique. The molecular models include both bottom-up and top-down approaches, including Coarse Grained (CG) force field development and force field tuning for complex multicomponent systems. See figure on the right and click here for the paper.

Materials Design Frameworks

Our lab utilizes hierarchical modeling techniques to design functional soft materials. For example, the simulated dynamics of a system of atoms over large spatiotemporal scales can be realized through coarse-grained (CG) modeling. The CG model development process roughly comprises: all atom reference simulations, statistical analysis, potential energy calculation, CG simulations, and CG model validation. These steps can be expensive, not only in the cost of running on high-performance compute (HPC) clusters but also in the time it takes to manage the intricate analysis operations. Our lab develops software frameworks to minimize these costs. Using modular tools for abstracting the "experiment-analysis" cycle, we aim to increase utilization of HPC resources, increase analysis throughput, and reduce the overhead associated with complex software toolchains. This is a collaborative effort with a group at Rutgers University. Here is the implementation and accompanying paper of this work. See figure on the right and click here for the paper.

Enhanced Sampling Techniques

The design of soft and biological materials often requires thorough characterization of the structure, conformation, and aggregate morphology of the constituent molecules. This characterization must include equilibrium conditions in the target biological environment, but it must also account for the chemical environment and energetics of manufacturing and processing. Our lab supports the requirements of these projects through the development and implementation of enhanced sampling techniques. One such technique is the temperature-based Replica Exchange Molecular Dynamics, used to evaluate a broader range of energies than what is normally available to a fixed-temperature MD system. This is a collaborative effort with a group at Rutgers University. See figure on the right and click here for the paper.