We are a computational and theoretical chemistry research group in the Department of Chemistry at the University of Wisconsin-Madison and a part of the Theoretical Chemistry Institute. Our interests lie in understanding the structure and dynamics of complex fluids in solution and in complex environments using the methods of equilibrium and non-equilibrium statistical mechanics. More specifically, our current research focuses on using liquid state theory, computer simulation, and machine learning to understand the structure, dynamics, and phase behavior of macromolecular liquids.
We use two complementary approaches: a phenomenological approach where we devise simple models to obtain insight into complex behavior and a bottom-up approach where we parameterize atomistic force-fields, then coarse-grain these to obtain realistic but efficient models for the molecular simulation of complex fluids. The main computer simulation techniques we employ are molecular dynamics, Brownian dynamics, and Monte Carlo methods. Our research is interdisciplinary, and we collaborate with experimentalists in the department as well as scientists in chemical engineering, physiology, and food science.
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Multi-Scale Modeling: From Atoms to the Continuum
In complex fluids, such as polymers and surfactants, a wide range of length-scales and time-scales are of interest. Chemical features on the length-scale of Angstroms can play an important role but structural properties on the length-scale of microns are often of interest. Similarly the longest relaxation times of a polymer can be very large, but are coupled to local dynamics on very short timescales. We are using a variety of methods, loosely called multi-scale modeling, to study systems over a large range of length and time scales.
Advances in computing have spurred the popularity of artificial intelligence and machine learning methods in the physical sciences. We are using supervised and un-supervised machine learning methods to study phase transitions in complex fluids and conformational transitions in macromolecules.
Polyelectrolytes and Coacervates
Charged polymers in solution, and mixtures of polymers of opposite charge, with associated counterions and added salt, are fascinating from a fundamental perspective, while also being of significant technological importance. We are using multi-scale modeling and machine learning to understand the behavior of charged macromolecules in solution.
Electrolytes for Lithium Battery Applications
Obtaining solvents for Lithium ion batteries that decrease the propensity for dendrite formation is of technological importance, as well as a fundamental curiosity. We are studying electrolytes from an atomistic perspective and dendrite growth from a much more coarse-grained perspective. Our goal is to connect these two approaches to obtain a fundamental understanding of the effect of chemical details in the electrolyte eon dendrite growth.
Ionic liquids and deep eutectic solvents are a fascinating class of solvents with numerous potential applications. We are studying the behavior of polymers in these solvents using simulations using first-principles force fields and machine learning.