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Angela Violi

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Angela Violi is a Professor in the Department of Mechanical Engineering, and adjunct faculty in Chemical Engineering, Biophysics, Macromolecular Science and Engineering, and Applied Physics. The research in the group of Violi is focused on the application of statistical mechanics and computational methods to chemically and physically oriented problems in nanomaterials and biology. The group investigates the formation mechanisms of nanomaterials for various applications, including energy and biomedical systems, and the dynamics of biological systems and their interactions with nanomaterials.

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Don Siegel

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Don Siegel is an Associate Professor affiliated with the Mechanical Engineering Department and the Department of Material Science and Engineering. His research targets the discovery, characterization, and understanding of novel materials for energy-related applications. These efforts primarily employ atomic scale modeling to predict thermodynamic properties and kinetics. These data provide the necessary ingredients for identifying performance limiting mechanisms and for the “virtual screening” of candidate compounds having desired properties. Prof. Siegel is currently exploring several varieties of energy storage materials, lightweight structural alloys, and materials suitable for use in carbon capture applications.

Atomic scale model of a liquid electrolyte/solid Li2O2 interface in a Li-air battery cathode.

Atomic scale model of a liquid electrolyte/solid Li2O2 interface in a Li-air battery cathode.

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Jeff Fessler

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Jeff Fessler is a Professor in the Department of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. His research interests include numerical optimization, inverse problems, image reconstruction, computational imaging, tomography, magnetic resonance imaging.  Most of these applications involve large problem sizes and parallel computing methods (cluster, cloud, GPU, SIMD, etc.) are needed.

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Katsuyo Thornton

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Thornton’s research focuses on computational and theoretical investigations of the evolution of microstructures and nanostructures during processing and operation of materials. These investigations facilitate the understanding of the underlying physics of materials and their performance, which will aid us in designing advanced materials with desirable properties and in developing manufacturing processes that would enable their fabrication. The topics include growth and coarsening of precipitates, evolution of morphologically and topologically complex systems, microstructure-based simulations of electrochemical systems such as batteries, and self-assembly of quantum dots and other nanoscale phenomena during heteroepitaxy of semiconductors.  These projects involve advanced computational methods and large-scale simulations performed on high-performance computational platforms, and insights provide a means for material design and optimization.

A snapshot from a simulation of charge-discharge process in a lithium-ion battery, based on an experimentally obtained microstructure.

A snapshot from a simulation of charge-discharge process in a lithium-ion battery, based on an experimentally obtained microstructure.

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Eric Johnsen

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His research interests lie in the development of numerical methods and models for massively parallel computations of fluid mechanics problems on modern computing architectures, including GPUs. He specifically focuses on high-order accurate finite difference/volume/element and spectral methods desgined for robust, accurate and efficient simulations. With his codes, he investigates the basic physics of multiphase flows, high-speed flows and shock waves, turbulence and mixing, interfacial instabilities, complex fluids and plasmas. Target applications include biomedical engineering, energy, aeronautics and naval engineering.

Time sequence showing turbulent mixing between two fluids of different densities. Vortical structures colored by density highlight how the mixing region grows while the turbulence decays. The results are obtained using direct numerical simulation (DNS), in which all dynamical scales are resolved.

Time sequence showing turbulent mixing between two fluids of different densities. Vortical structures colored by density highlight how the mixing region grows while the turbulence decays. The results are obtained using direct numerical simulation (DNS), in which all dynamical scales are resolved.

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Emmanouil (Manos) Kioupakis

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His group uses first-principles computational methods and high-performance computing resources to predictively model the structural, electronic, and optical properties of bulk materials and nanostructures. The goal is to understand, predict, and optimize the properties of novel electronic, optoelectronic, photovoltaic, and thermoelectric materials.

The Kioupakis group uses high-performance computing to predictively model the electronic and optical properties of semiconductor nanostructures such as nanoporous silicon, nitride nanowires, and novel 2D materials.

The Kioupakis group uses high-performance computing to predictively model the electronic and optical properties of semiconductor nanostructures such as nanoporous silicon, nitride nanowires, and novel 2D materials.

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Eitan Geva

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Eitan Geva is a Professor of Chemistry. Modern computational chemistry strives to provide an atomistically detailed dynamical description of fundamental chemical processes. The strategy for reaching this goal generally follows a two-step program. In the first step, electronic structure calculations are used to obtain the force fields that the nuclei are subject to. In the second step, molecular dynamics simulations are used to describe the motion of the nuclei. The first step is always based on quantum mechanics, in the light of the pronounced quantum nature of the electrons. However, the second step is most often based on classical mechanics. Indeed, classical molecular dynamics simulations are rroutinely used nowadays for describing the dynamics of complex chemical systems that involve tens of thousands of atoms. However, there are many important situations where classical mechanics cannot be used for describing the dynamics. Our research targets the most chemically relevant examples of such processes.
  1. Vibrational end electronic relaxation. The pathways of intramolecular energy redistribution within molecules and intermolecular energy transfer between molecules, which dictate chemical reactivity, are governed by the rates of these processes. The pronounced quantum nature of these processes is attributed to the large gap between vibrational and electronic energy levels.
  2. Proton and electron transfer reactions. The elementary steps of many complex chemical processes are based on such reactions. Their pronounced quantum nature is attributed to the light mass of protons and electrons, which often give rise to quantum tunneling and zero-point energy effects.
  3. Nonadiabatic dynamics. Such dynamics underlie photochemistry and nonlinear spectroscopy is quantum in nature since it involves simultaneous motion on several potential surfaces that correspond to different electronic or vibrational states.

The challenge involved in simulating the quantum molecular dynamics of such systems has to do with the fact that the computational effort involved in solving the time-dependent Schrodinger equation is exponentially larger than that involved in Newton’s equations. As a result, a numerically exact solution of the Schrondinger equation is not feasible for a system that consists of more than a few atoms. The main research thrust of the Geva group is aimed at developing rigorous and accurate mixed quantum-classical, quasi-classical and semiclassical methods that would make it possible to simulate equilibrium and nonequilibrium quantum dynamics of systems that consist of hundreds of atoms and molecules. We put emphasis on applications to experimentally-relevant disordered complex condensed phase systems such as molecular liquids, which serve as hosts for many important chemical processes. We also specialize in modeling and analyzing different types of time resolved electronic and vibrational spectra that are used to probe molecular dynamics in those systems, often in collaboration with experimental groups.

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Sharon Glotzer

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Sharon Glotzer is a Professor of Chemical Engineering and of Material Science and Engineering. The Glotzer group uses computer simulations to discover the fundamental principles by which nanoscale systems of building blocks self-assemble into higher order, complex, and often hierarchical structures. Their goal is to learn how to manipulate matter at the molecular, nanoparticle, and colloidal scales to create “designer” structures through assembly engineering. Using molecular dynamics and Monte Carlo simulation codes developed in-house for graphics processors (GPUs) and scalable to large hybrid CPU/GPU clusters, they are the leading computational assembly group in the world, with the most powerful codes for studying assembly and packing. Among others, they are the lead developer of HOOMD-Blue, the fastest molecular dynamics code written solely for GPUs and distributed freely as open source software on codeblue.umich.edu.  Based on the fundament scientific principles of assembly gleaned from their studies, they carry out high throughout simulations for materials by design, contributing to the national Materials Genome Initiative.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

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Brian Arbic

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Brian Arbic is an Associate Professor in the Department of Earth and Environmental Sciences, with an appointment in the Department of Climate and Space Sciences Engineering and affiliations with Applied and Interdisciplinary Mathematics, Applied Physics, and the Center for the Study of Complex Systems. Arbic is a physical oceanographer primarily interested in the dynamics and energy budgets of oceanic mesoscale eddies (the oceanic equivalent of atmospheric weather systems), the large-scale oceanic general circulation, and tides. He has also studied paleotides, tsunamis, and the decadal variability of subsurface ocean temperatures and salinities. His primary tools are numerical models of the ocean. Arbic uses both realistic models, such as the HYbrid Coordinate Ocean Model (HYCOM) being used as a U.S. Navy ocean forecast model, and idealized models. He frequently compares the outputs of such models to oceanic observations, taken with a variety of instruments. Comparison of models and observations helps us to improve models and ideas about how the ocean works. His research has often been interdisciplinary, involving collaborations with scientists outside of my discipline, such as glaciologists, geodynamicists, and marine geophysicists.

The surface expression of the M_2 principal lunar semidiurnal internal tide — the tide that arises due to the stratification of the ocean. The top panel shows analysis of satellite altimetry data, while the bottom shows results from HYCOM, run by collaborators at the Naval Research Laboratory. (Shriver, et al 2012)

The surface expression of the M_2 principal lunar semidiurnal internal tide — the tide that arises due to the stratification of the ocean. The top panel shows analysis of satellite altimetry data, while the bottom shows results from HYCOM, run by collaborators at the Naval Research Laboratory. (Shriver, et al 2012)

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Charles Brooks

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Charles L. Brooks III is the Warner-Lambert/Parke-Davis Professor of Chemistry and a Professor of Biophysics. He is affiliated with the department of Chemistry, Biophysics Program, program in Applied Physics, Molecular Biophysics Training Program (Director), program in Chemical Biology, Bioinformatics Graduate Program, Center for Computational Medicine and Bioinformatics and the Medicinal Chemistry Interdepartmental Graduate Program. The research in the group of Charles L. Brooks III is focused on the application of statistical mechanics, quantum chemistry and computational methods to chemically and physically oriented problems in biology. The group develops and applies computational models to studies of the dynamics of proteins, nucleic acids and their complexes, including virus structure and assembly. They specifically develop novel computational methods for the inclusion of pH effects in modeling biological systems. Significant focus is in the development of a large, world-wide distributed software package for molecular simulations, CHARMM. Efforts are ongoing to explore new means of parallel and accelerated computation utilizing scalable parallel algorithms for molecular dynamics and integrated CPU/GPU computational models.