Ronald Gary Larson

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Larson’s research has been in the area of “Complex Fluids,” which include polymers, colloids, surfactant-containing fluids, liquid crystals, and biological macromolecules such as DNA, proteins, and lipid membranes. He has also contributed extensively to fluid mechanics, including microfluidics, and transport modeling. He has also has carried out research over the past 16 years in the area of molecular simulations for biomedical applications. The work has involved determining the structure and dynamics of lipid membranes, trans-membrane peptides, anti-microbial peptides, the conformation and functioning of ion channels, interactions of excipients with drugs for drug delivery, interactions of peptides with proteins including MHC molecules, resulting in more than 50 publications in these areas, and in the training of several Ph.D. students and postdocs. Many of these studies involve heavy use of computer simulations and methods of statistical analysis of simulations, including umbrella sampling, forward flux sampling, and metadynamics, which involve statistical weighting of results. He also has been engaged in analysis of percolation processes on lattices, including application to disease propagation.

Alpha helical peptide bridging lipid bilayer in molecular dynamics simulations of “hydrophobic mismatch.”

Robert Ziff

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I study the percolation model, which is the model for long-range connectivity formation in systems that include polymerization, flow in porous media, cell-phone signals, and the spread of diseases. I study this on random graphs and other networks, and on regular lattices in various dimensions, using computer simulation and analysis. We have also worked on developing new algorithms. I am currently applying these methods to studying the COVID-19 pandemic, which also requires comparison with some of the vast amount of data that is available from every part of the world.

 

Heather B. Mayes

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Heather B. Mayes, PhD, is Assistant Professor of Chemical Engineering in the College of Engineering at The University of Michigan, Ann Arbor.

The Team Mayes and Blue focuses on discovering fundamental structure-function relationships that govern how proteins and sugars interact in applications from renewable materials to human health. We use atomistic simulation (molecular mechanics and quantum mechanics) to determine the fundamental, microscopic interactions that determine macroscopically observable phenomena. The resulting mechanistic understanding is harnessed to engineer more efficient proteins to meet biotechnology needs, whether to break down biomass to create feedstock for renewable fuels and chemicals, or create prebiotic carbohydrates.

Molecular simulations allow us to discover fundamental mechanistic processes, such as the overall energies associated with carbohydrate procession into an enzyme (A), and the individual structural components governing the mechanism, such as electrostatic interactions as a function of position (B). These simulations create rich data sets from which we can determine these structure-function relationships and use them to make predictions of how mutations to proteins can change function, thus enabling rational enzyme design.

 

Bryan R. Goldsmith

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Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

Sharon Glotzer

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Sharon Glotzer, PhD, is the Anthony C. Lembke Department Chair of Chemical Engineering, the John Werner Cahn Distinguished University Professor of Engineering, and the Stuart W. Churchill Collegiate Professor of Chemical Engineering, in the College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Glotzer and her group are focused on the new revolution in nano-science, engineering and technology is being driven by our ability to manipulate matter at the molecular, nanoparticle, and colloidal level to create “designer” structures. The Glotzer group uses computer simulation to discover the fundamental principles of how nanoscale systems of building blocks self-assemble, and to discover how to control the assembly process to engineer new materials. By mimicking biological assembly, we are exploring ways to nano-engineer materials that are self-assembling, self-sensing, and self-regulating.

The group is developing theory and molecular simulation tools to understand these materials, and elucidate the nature of supercooled liquids, glasses, and crystallization.

The Glotzer group develops in-house, open-source software for simulation, data analysis, and more. We invite the scientific community to learn more and utilize our software.