Don Siegel

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Don Siegel is a Professor in the Department of Mechanical Engineering 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.

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.

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.

Vikram Gavini

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His research group aims to develop computational and mathematical techniques to address various aspects of materials behavior, which exhibit complexity and structure on varying length and time scales. The work draws ideas from quantum mechanics, statistical mechanics and homogenization theories to create multi-scale models from fundamental principles, which provide insight into the complex behavior of materials. Topics of research include developing multi-scale methods for density-functional theory (electronic structure) calculations at continuum scales, electronic structure studies on defects in materials, quasi-continuum method, analysis of approximation theories, numerical analysis, and quantum transport in materials.

Hierarchy of triangulations that form the basis of a coarse-graining methods (quasi-continuum reduction) for conducting electronic structure calculations at macroscopic scales.

Hierarchy of triangulations that form the basis of a coarse-graining methods (quasi-continuum reduction) for conducting electronic structure calculations at macroscopic scales.

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.