PascalVanHentenryck

Pascal Van Hetenryck

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Pascal Van Hentenryck is the Seth Bonder Collegiate Professor of Industrial & Operations Engineering.

Prof. Van Hentenryck’s research is currently at the intersection of data science and optimization with a focus on risk and resilience, energy systems, transportation, and logistics, marketing, and social networks. Most of these applications require predictive models and optimization over complex infrastructures, natural phenomena, and human behavior.

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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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Kai Sun

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His  research focuses on understanding the role of strong correction effects in many-body quantum systems. The objective is to discover novel quantum states/materials and to understand their exotic properties using theoretical/numerical methods (with emphasis on topological properties). In his research, numerical techniques are applied to resolve the fate of a quantum material (or a theoretical model) in the presence of multiple competing ground states and to provide quantitative guidance for further (experimental/theoretical) investigations.

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Interaction induced topological insulator with spontaneously-generated orbital rotations. This figure demonstrate how to use strong interactions to generate a topological state of matter in a many-body quantum system.

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Udo Becker

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Professor Becker leads an interdisciplinary group to understand problems in surface geochemistry and computational mineralogy, thus there are research opportunities in fields ranging from experimental approaches and computational modeling of actinide geochemistry (U immobilization in the environment, actinide-containing solids under extreme pressure, temperature, and radiation, U/Np/Pu redox processes) to carbonate biomineralization. Other research includes calculating redox processes (including resolving individual kinetic barriers that control kinetics) carbonate and phosphate biomineralization (from environmental applications to processes on teeth). As a part of Mineralogy and Materials Science Research Group, Becker’s group  interacts with Radiation Effects and Radioactive Waste Management group, Michigan Geomicrobiology group, Electron Microbeam Analysis Laboratory (EMAL) and Mineral Physics group.

Band decomposed charge density associated with the defect state at -0.7eV, introduced by Pu occupying the A site in Ca3Zr2(FE2Si)O12.

Band decomposed charge density associated with the defect state at -0.7eV, introduced by Pu occupying the A site in Ca3Zr2(FE2Si)O12.

 

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Thomas Wenisch

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His research interests center on computer architecture with particular emphasis on multiprocessor and multicore systems,  data center architecture, architectural support for medical imaging, and performance evaluation methodology.

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Robert Ziff

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Professor Ziff carries out computational and theoretical studies of various physical problems, most notably percolation but also catalysis modeling and several reaction/diffusion systems.  For percolation, he has developed various algorithms that have allowed substantial increases in performance, for the study of threshold behavior, crossing probability, etc. He also studies algorithms for efficiently simulating rare-event simulations such as chemical reactions and diffusion-limited aggregation.

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Inside a diffusion-limited aggregation (DLA) cluster, grown using an accelerated rare-event algorithm.

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Paul Zimmerman

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From elementary chemical reactions to exciton dynamics in solar cells, chemistry is a particularly rich field for atomistic simulation. Research in the Zimmerman group develops and employs a broad spectrum of computational techniques to chemical problems. Special emphasis is taken on creating new, practical computational methods for application to problems that are considered out-of-reach to standard simulation methodologies. For instance, automated prediction of chemical reactions has long been considered impossible using quantum chemical simulation. To break this limitation, the Zimmerman group is creating new techniques for locating reaction paths and products of catalytic reactions, with the goal of predicting the outcome of reactions prior to experiment. These tools use a combination of chemical intuition, applied mathematics, and massively parallel computation to achieve an impressive level of automation and predictive value.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

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Kazuhiro Saitou

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His research group investigates simulation-based and data-driven computational synthesis of for mechanical, industrial and biomedical systems. The target systems are modeled by utilizing tools and algorithms in computational mechanics, geometric reasoning, image recognition, statistical data processing, and optimized by numerical optimization algorithms. Recent application domains includes lightweight automotive structures, intelligent transportation systems, water desalination systems, energy-efficient production systems, biomedical deformable image registration, and statistical protein energy potentials.

Solar-powered desalination systems for resource-restricted environment. Numerical simulation, optimization, and data mining techniques are utilized to synthesize decision trees among feasible technology alternatives for water desalination systems in rural communities with limited infrastructure access.

Solar-powered desalination systems for resource-restricted environment. Numerical simulation, optimization, and data mining techniques are utilized to synthesize decision trees among feasible technology alternatives for water desalination systems in rural communities with limited infrastructure access.

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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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Heath Hofmann

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Heath Hofmann is a Professor of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. Professor Hofmann’s computational research focuses on the modelling of electromechanical devices and systems. An area of emphasis is the development of computationally efficient electromagnetic and thermal models of rotating electric machines based upon finite element analysis (FEA). Specific projects include the development of parallelizable preconditioners for steady-state magnetoquasistatic FEA solvers, the application of model-order-reduction techniques to thermal and electromagnetic finite-element models, nonlinear modeling of magnetic materials, integrated FEA-circuit simulations, and the development of “scaling” techniques that allow the user to efficiently create a suite of electric machines with different performance characteristics from a single design.

Magnetoquasistatic model of permanent magnet machine

Magnetoquasistatic model of permanent magnet machine