Research

Research in the Webb group centers on accurate and chemically specific computational approaches to simulate, characterize, and design novel materials for health and sustainability applications. We are particularly interested in the study of both natural and synthetic polymers, which have applications that range from simple thickening agents in foods to electrolyte solvents in batteries to stimuli-responsive drug-delivery systems. This versatility inherent to polymeric materials can be largely attributed to their macromolecular nature in combination of chemical and topological diversity, which presents both exciting engineering opportunities as well as complex challenges for molecular simulation.

Central questions to our research are how does the chemistry matter and how can we effectively describe it across multiple spatiotemporal scales? Through the development and application of hierarchical simulation techniques, we aim to provide chemical insights on experimental metrology and guide the design of future functional materials.

Designing Polymeric Membranes for Sustainability Applications

The technological viability of polymeric materials in various sustainability applications depends on myriad factors related to chemistry, morphology, and processing that affect device performance. Using a range of simulation techniques, we are investigating essential physics underlying experimental figures of merit in polymeric media and nanostructured materials for applications such as fuel cells, batteries, and water-treatment membranes. We aim to translate fundamental and comparative studies into design principles and efficient modeling platforms.

Controlling Biopolymer Interactions for Health Applications

Biopolymers, like polysaccharides and polypeptides, are attractive candidate materials due to inherent biocompatibility, renewability, and availability. Moreover, many chemistries exhibit stimuli-responsive behavior that can be manipulated to achieve functional, adaptive materials. We are developing systematic coarse-graining workflows to effectively describe interactions of biopolymers and other nanomaterials in diverse environments.

Advancing Hierarchical Simulation Capabilities

Systematic coarse-graining methodologies

A key challenge in soft matter simulation is the need to link underlying chemistry to relevant materials properties. Atomistic-resolution simulations might accurately reflect chemistry, but they can be limited to reporting on structures on the order of nanometers or dynamics of hundreds of nanoseconds. Meanwhile, simplified models, like polymer bead-spring models, might describe experimentally relevant length- and timescales, but they generally do not reflect any particular chemistry. We are thus generally interested in developing methods or constructing approaches that enable more accurate, sophisticated coarse-grained simulations. This will yield models that better interface with and utilize experimental feedback.

Integrating Machine Learning & Modeling in Soft Materials Design

Applications of Machine Learning for Soft Materials

While machine learning algorithms have emerged as powerful tools for molecular property prediction and design for many problems in chemistry, engineering, and materials science--successful application of ML to problems in soft materials has been much more limited. With the aid of our systematic coarse-graining techniques, we are exploring, integrating, and exploiting ML techniques in tandem with soft matter simulation to facilitate materials design.